text:
{quModifiedSineCosineAlgorithm2018a,
  title = {A {{Modified Sine}}-{{Cosine Algorithm Based}} on {{Neighborhood Search}} and {{Greedy Levy Mutation}}},
  author = {Qu, Chiwen and Zeng, Zhiliu and Dai, Jun and Yi, Zhongjun and He, Wei},
  year = {2018},
  doi = {10.1155/2018/4231647},
  abstract = {For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\P27BPW5Q\\4231647.html:application/xhtml+xml;Full Text PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\R8HFSJLR\\Qu et al. - 2018 - A Modified Sine-Cosine Algorithm Based on Neighbor.pdf:application/pdf},
  journal = {Computational Intelligence and Neuroscience},
  language = {en},
  timestamp = {2019-08-17T04:37:52Z},
  type = {Research Article}
}

@article{rairanRobotPathOptimization2015,
  title = {Robot {{Path Optimization Based}} on a {{Reference Model}} and {{Sigmoid Functions}}},
  author = {Rairan, Jose Danilo},
  year = {2015},
  month = mar,
  volume = {12},
  pages = {19},
  publisher = {{SAGE Publications}},
  issn = {1729-8814},
  doi = {10.5772/60063},
  abstract = {The purpose of this study is to minimize the arc length for the path described by the model of a robot platform when the path is constrained to have smooth transitions given by sigmoid functions. The optimization required a proof of stability for the resulting control law, the selection of the best sigmoid function among ten functions, and the definition of two gains necessary to parameterize the control law. The optimization was carried out by simulating the system under several kinematic and dynamical conditions, and the best sigmoid function was a hyperbolic tangent. Thus, the motion control first implied the simulation of a reference model to define an optimal path, and later the control of an actual robot platform, which followed the optimal path. The use of the optimized path reduced the complexity of the controller while allowing natural and intuitive paths for the robot platform.},
  file = {SAGE PDF Full Text:C\:\\Users\\SomefunAgba\\Zotero\\storage\\TM2H6AWM\\Rairan - 2015 - Robot Path Optimization Based on a Reference Model.pdf:application/pdf},
  journal = {International Journal of Advanced Robotic Systems},
  language = {en},
  number = {3},
  timestamp = {2020-07-10T04:25:43Z}
}

@article{reedSummationLogisticCurves1927,
  title = {On the {{Summation}} of {{Logistic Curves}}},
  author = {Reed, Lowell J. and Pearl, Raymond},
  year = {1927},
  volume = {90},
  pages = {729--746},
  publisher = {{[Wiley, Royal Statistical Society]}},
  issn = {0952-8385},
  doi = {10.2307/2341367},
  journal = {Journal of the Royal Statistical Society},
  number = {4},
  timestamp = {2020-10-22T13:04:24Z}
}

@article{roodschildNewApproachVanishing2020,
  title = {A New Approach for the Vanishing Gradient Problem on Sigmoid Activation},
  author = {Roodschild, Mat{\'i}as and Gotay Sardi{\~n}as, Jorge and Will, Adri{\'a}n},
  year = {2020},
  month = dec,
  volume = {9},
  pages = {351--360},
  issn = {2192-6352, 2192-6360},
  doi = {10.1007/s13748-020-00218-y},
  journal = {Progress in Artificial Intelligence},
  language = {en},
  number = {4},
  timestamp = {2020-12-14T05:15:01Z}
}

@article{roserCoronavirusPandemicCOVID192020,
  title = {Coronavirus {{Pandemic}} ({{COVID}}-19)},
  author = {Roser, Max and Ritchie, Hannah and {Ortiz-Ospina}, Esteban and Hasell, Joe},
  year = {2020},
  month = may,
  url = {https://ourworldindata.org/coronavirus/country/nigeria},
  urldate = {2020-10-19},
  abstract = {Nigeria: What has been the impact of the Coronavirus Pandemic (COVID-19)?},
  journal = {Our World in Data},
  timestamp = {2020-10-19T08:12:14Z}
}

@inproceedings{sartin_approximation_2013,
  title = {Approximation of Hyperbolic Tangent Activation Function Using Hybrid Methods},
  booktitle = {2013 8th {{International Workshop}} on {{Reconfigurable}} and {{Communication}}-{{Centric Systems}}-on-{{Chip}} ({{ReCoSoC}})},
  author = {Sartin, M. A. and da Silva, A. C. R.},
  year = {2013},
  month = jul,
  pages = {1--6},
  doi = {10.1109/ReCoSoC.2013.6581545},
  abstract = {Artificial Neural Networks are widely used in various applications in engineering, as such solutions of nonlinear problems. The implementation of this technique in reconfigurable devices is a great challenge to researchers by several factors, such as floating point precision, nonlinear activation function, performance and area used in FPGA. The contribution of this work is the approximation of a nonlinear function used in ANN, the popular hyperbolic tangent activation function. The system architecture is composed of several scenarios that provide a tradeoff of performance, precision and area used in FPGA. The results are compared in different scenarios and with current literature on error analysis, area and system performance.},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\SomefunAgba\\Zotero\\storage\\3C4ZRCCQ\\6581545.html:text/html},
  timestamp = {2019-05-06T19:56:07Z}
}

@article{saulMeanFieldTheory1996,
  title = {Mean Field Theory for Sigmoid Belief Networks},
  author = {Saul, Lawrence K. and Jaakkola, Tommi and Jordan, Michael I.},
  year = {1996},
  volume = {4},
  pages = {61--76},
  file = {Full Text:C\:\\Users\\SomefunAgba\\Zotero\\storage\\V89VH56N\\Saul et al. - 1996 - Mean field theory for sigmoid belief networks.pdf:application/pdf;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\ZE7NYWSH\\10156.html:text/html},
  journal = {Journal of artificial intelligence research},
  timestamp = {2020-11-29T11:29:12Z}
}

@article{schmidhuberDeepLearningNeural2015,
  title = {Deep Learning in Neural Networks: {{An}} Overview},
  shorttitle = {Deep Learning in Neural Networks},
  author = {Schmidhuber, J{\"u}rgen},
  year = {2015},
  month = jan,
  volume = {61},
  pages = {85--117},
  issn = {0893-6080},
  doi = {10.1016/j.neunet.2014.09.003},
  abstract = {In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning \& evolutionary computation, and indirect search for short programs encoding deep and large networks.},
  file = {Submitted Version:C\:\\Users\\SomefunAgba\\Zotero\\storage\\HGPQSSB2\\Schmidhuber - 2015 - Deep learning in neural networks An overview.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\AR2SSVJC\\S0893608014002135.html:text/html},
  journal = {Neural Networks},
  language = {en},
  timestamp = {2020-07-06T05:17:08Z}
}

@article{sejnowskiUnreasonableEffectivenessDeep2020,
  title = {The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence},
  author = {Sejnowski, Terrence J.},
  year = {2020},
  month = dec,
  volume = {117},
  pages = {30033--30038},
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1907373117},
  abstract = {Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and nonconvex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures, and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals.},
  chapter = {Colloquium Paper},
  copyright = {\textcopyright{} 2020 . https://www.pnas.org/site/aboutpnas/licenses.xhtmlPublished under the PNAS license.},
  file = {Full Text PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\2TLSGRZ8\\Sejnowski - 2020 - The unreasonable effectiveness of deep learning in.pdf:application/pdf;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\63XCUQZ5\\30033.html:text/html;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\M5I8GUA2\\30033.html:text/html},
  journal = {Proceedings of the National Academy of Sciences},
  language = {en},
  number = {48},
  pmid = {31992643},
  timestamp = {2020-12-09T19:55:25Z}
}

@article{shaoNonlinearTrackingDifferentiator20140901,
  title = {{Nonlinear tracking differentiator based on improved sigmoid function}},
  author = {Shao, Xingling and Wang, Honglun},
  year = {20140901},
  volume = {31},
  pages = {1116--1122},
  doi = {10.7641/CTA.2014.31117},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\7GMPRNMM\\view_abstract.html:text/html},
  journal = {Control Theory and Application},
  language = {cn},
  number = {8},
  timestamp = {2020-09-27T01:35:27Z}
}

@article{sharifianVelocityControlDC2009,
  title = {Velocity {{Control}} of {{DC Motor Based Intelligent Methods}} and {{Optimal Integral State Feed Back Controller}}},
  author = {Sharifian, M.B.B. and Rahnavard, R. and Delavari, H.},
  year = {2009},
  pages = {81--84},
  issn = {17938201},
  doi = {10.7763/IJCTE.2009.V1.13},
  journal = {International Journal of Computer Theory and Engineering},
  timestamp = {2019-08-18T03:11:07Z}
}

@incollection{sharmaIntelligentAdaptiveFuzzy2018,
  title = {Intelligent {{Adaptive Fuzzy Control}}},
  booktitle = {Intelligent {{Control}}},
  author = {Sharma, Kaushik Das and Chatterjee, Amitava and Rakshit, Anjan},
  year = {2018},
  pages = {3--21},
  publisher = {{Springer}},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\YJYUW3ZZ\\978-981-13-1298-4_1.html:text/html},
  timestamp = {2019-08-03T04:42:53Z}
}

@article{shenLogisticGrowthModelling2020,
  title = {Logistic Growth Modelling of {{COVID}}-19 Proliferation in {{China}} and Its International Implications},
  author = {Shen, Christopher Y.},
  year = {2020},
  month = jul,
  volume = {96},
  pages = {582--589},
  issn = {1201-9712},
  doi = {10.1016/j.ijid.2020.04.085},
  abstract = {Objective As the coronavirus disease 2019 (COVID-19) pandemic continues to proliferate globally, this paper shares the findings of modelling the outbreak in China at both provincial and national levels. This paper examines the applicability of the logistic growth model, with implications for the study of the COVID-19 pandemic and other infectious diseases. Methods An NLS (Non-Linear Least Squares) method was employed to estimate the parameters of a differentiated logistic growth function using new daily COVID-19 cases in multiple regions in China and in other selected countries. The estimation was based upon training data from January 20, 2020 to March 13, 2020. A restriction test was subsequently implemented to examine whether a designated parameter was identical among regions or countries, and the diagnosis of residuals was also conducted. The model's goodness of fit was checked using testing data from March 14, 2020 to April 18, 2020. Results The model presented in this paper fitted time-series data exceedingly well for the whole of China, its eleven selected provinces and municipalities, and two other countries - South Korea and Iran - and provided estimates of key parameters. This study rejected the null hypothesis that the growth rates of outbreaks were the same among ten selected non-Hubei provinces in China, as well as between South Korea and Iran. The study found that the model did not provide reliable estimates for countries that were in the early stages of outbreaks. Furthermore, this study concured that the R2 values might vary and mislead when compared between different portions of the same non-linear curve. In addition, the study identified the existence of heteroskedasticity and positive serial correlation within residuals in some provinces and countries. Conclusions The findings suggest that there is potential for this model to contribute to better public health policy in combatting COVID-19. The model does so by providing a simple logistic framework for retrospectively analyzing outbreaks in regions that have already experienced a maximal proliferation in cases. Based upon statistical findings, this study also outlines certain challenges in modelling and their implications for the results.},
  file = {ScienceDirect Full Text PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\3TYAKJ3F\\Shen - 2020 - Logistic growth modelling of COVID-19 proliferatio.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\NHR2R627\\S1201971220303039.html:text/html},
  journal = {International Journal of Infectious Diseases},
  language = {en},
  timestamp = {2020-10-19T06:21:16Z}
}

@article{shulmanMathAliveUsingOriginal1998,
  title = {Math-{{Alive}}! {{Using Original Sources To Teach Mathematics In Social Context}}},
  author = {Shulman, Bonnie},
  year = {1998},
  month = jan,
  volume = {8},
  pages = {1--14},
  issn = {1051-1970, 1935-4053},
  doi = {10.1080/10511979808965879},
  journal = {PRIMUS},
  language = {en},
  number = {1},
  timestamp = {2020-11-30T06:39:49Z}
}

@article{simardBestPracticesConvolutional2003,
  title = {Best {{Practices}} for {{Convolutional Neural Networks Applied}} to {{Visual Document Analysis}}},
  author = {Simard, Patrice Y. and Steinkraus, Dave and Platt, John},
  year = {2003},
  month = aug,
  url = {https://www.microsoft.com/en-us/research/publication/best-practices-for-convolutional-neural-networks-applied-to-visual-document-analysis/},
  urldate = {2020-10-20},
  abstract = {Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural [\ldots ]},
  file = {Full Text PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\AY2WWBA7\\Simard et al. - 2003 - Best Practices for Convolutional Neural Networks A.pdf:application/pdf;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\6T2F3UVD\\best-practices-for-convolutional-neural-networks-applied-to-visual-document-analysis.html:text/html},
  language = {en-US},
  timestamp = {2020-10-20T09:49:55Z}
}

@book{sobhani-tehraniFaultDiagnosisNonlinear2009,
  title = {Fault {{Diagnosis}} of {{Nonlinear Systems Using}} a {{Hybrid Approach}}},
  author = {{Sobhani-Tehrani}, Ehsan and Khorasani, Khashayar},
  year = {2009},
  month = jun,
  publisher = {{Springer Science \& Business Media}},
  abstract = {Theincreasingcomplexityofspacevehiclessuchassatellites,andthecostreduction measures that have affected satellite operators are increasingly driving the need for more autonomy in satellite diagnostics and control systems. Current methods for detecting and correcting anomalies onboard the spacecraft as well as on the ground are primarily manual and labor intensive, and therefore, tend to be slow. Operators inspect telemetry data to determine the current satellite health. They use various statisticaltechniques andmodels,buttheanalysisandevaluation ofthelargevolume of data still require extensive human intervention and expertise that is prone to error. Furthermore, for spacecraft and most of these satellites, there can be potentially unduly long delays in round-trip communications between the ground station and the satellite. In this context, it is desirable to have onboard fault-diagnosis system that is capable of detecting, isolating, identifying or classifying faults in the system withouttheinvolvementandinterventionofoperators.Towardthisend,theprinciple goal here is to improve the ef?ciency, accuracy, and reliability of the trend analysis and diagnostics techniques through utilization of intelligent-based and hybrid-based methodologies.},
  googlebooks = {6OH0GX1oQd8C},
  isbn = {978-0-387-92906-4},
  language = {en},
  timestamp = {2019-08-04T03:22:06Z}
}

@inproceedings{somefunetalnlsigcovid19tyanconf2020,
  title = {On the Nlogistic-Sigmoid Modelling for Complex Growth Processes: In Application to the {{COVID}}-19 Pandemic},
  booktitle = {6th {{TYAN International Thematic Workshop}} on {{Data Science}} for {{Solution}}-Driven and {{Sustainable Response}} to Current Developing World Challenges},
  author = {{Somefun} and O. A., Akingbade, K. F. and Dahunsi, F. M.},
  year = {2020},
  month = dec,
  timestamp = {2020-12-08T13:30:35Z}
}

@article{somefunLogisticsigmoidNlogisticsigmoidModelling2020,
  title = {From the Logistic-Sigmoid to Nlogistic-Sigmoid: Modelling the {{COVID}}-19 Pandemic Growth},
  author = {Somefun, Oluwasegun A. and Akingbade, Kayode and Dahunsi, Folasade},
  year = {2020},
  month = dec,
  url = {http://arxiv.org/abs/2008.04210},
  urldate = {2020-12-15},
  archivePrefix = {arXiv},
  eprint = {2008.04210},
  eprinttype = {arxiv},
  journal = {arXiv:2008.04210 [cs]},
  primaryClass = {cs},
  timestamp = {2020-12-15T21:51:35Z}
}

@misc{somefunSomefunAgbaNLSIGCOVID19Lab2020,
  title = {{{somefunAgba}}/{{NLSIG}}\_{{COVID19Lab}}},
  author = {Somefun, Oluwasegun},
  year = {2020},
  month = dec,
  url = {https://github.com/somefunAgba/NLSIG_COVID19Lab},
  urldate = {2020-12-08},
  abstract = {A nlogistic-sigmoid modelling laboratory for the COVID-19 pandemic growth},
  copyright = {AGPL},
  timestamp = {2020-12-08T13:26:41Z}
}

@inproceedings{sopenaNeuralNetworksPeriodic1999,
  title = {Neural Networks with Periodic and Monotonic Activation Functions: A Comparative Study in Classification Problems},
  shorttitle = {Neural Networks with Periodic and Monotonic Activation Functions},
  booktitle = {9th {{International Conference}} on {{Artificial Neural Networks}}: {{ICANN}} '99},
  author = {Sopena, J.M.},
  year = {1999},
  volume = {1999},
  pages = {323--328},
  publisher = {{IEE}},
  address = {{Edinburgh, UK}},
  doi = {10.1049/cp:19991129},
  isbn = {978-0-85296-721-8},
  language = {en},
  timestamp = {2019-07-29T18:28:32Z}
}

@book{stanoyevitchIntroductionNumericalOrdinary2004,
  title = {Introduction to {{Numerical Ordinary}} and {{Partial Differential Equations Using MATLAB}}},
  author = {Stanoyevitch, Alexander},
  year = {2004},
  edition = {First},
  publisher = {{Wiley-Interscience}},
  url = {http://gen.lib.rus.ec/book/index.php?md5=9244d0623ceffcefe98fbd10aa7f0116},
  urldate = {2019-08-04},
  isbn = {978-0-471-69738-1},
  series = {Pure and {{Applied Mathematics}}: {{A Wiley Series}} of {{Texts}}, {{Monographs}} and {{Tracts}}},
  timestamp = {2019-08-04T17:26:12Z}
}

@article{szeEfficientProcessingDeep2017,
  title = {Efficient {{Processing}} of {{Deep Neural Networks}}: {{A Tutorial}} and {{Survey}}},
  shorttitle = {Efficient {{Processing}} of {{Deep Neural Networks}}},
  author = {Sze, Vivienne and Chen, YuHsin and Yang, TienJu and Emer, Joel S.},
  year = {2017},
  month = dec,
  volume = {105},
  pages = {2295--2329},
  issn = {1558-2256},
  doi = {10.1109/JPROC.2017.2761740},
  abstract = {Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.},
  journal = {Proceedings of the IEEE},
  number = {12},
  timestamp = {2020-09-27T02:18:23Z}
}

@article{tabatabaiHyperbolasticGrowthModels2005,
  title = {Hyperbolastic Growth Models: Theory and Application},
  shorttitle = {Hyperbolastic Growth Models},
  author = {Tabatabai, Mohammad and Williams, David Keith and Bursac, Zoran},
  year = {2005},
  month = mar,
  volume = {2},
  pages = {14},
  issn = {1742-4682},
  doi = {10.1186/1742-4682-2-14},
  abstract = {Mathematical models describing growth kinetics are very important for predicting many biological phenomena such as tumor volume, speed of disease progression, and determination of an optimal radiation and/or chemotherapy schedule. Growth models such as logistic, Gompertz, Richards, and Weibull have been extensively studied and applied to a wide range of medical and biological studies. We introduce a class of three and four parameter models called "hyperbolastic models" for accurately predicting and analyzing self-limited growth behavior that occurs e.g. in tumors. To illustrate the application and utility of these models and to gain a more complete understanding of them, we apply them to two sets of data considered in previously published literature.},
  file = {Full Text:C\:\\Users\\SomefunAgba\\Zotero\\storage\\XIKVJWTB\\Tabatabai et al. - 2005 - Hyperbolastic growth models theory and applicatio.pdf:application/pdf;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\KSFFZDZF\\1742-4682-2-14.html:text/html;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\Q84DQTXQ\\1742-4682-2-14.html:text/html},
  journal = {Theoretical Biology and Medical Modelling},
  number = {1},
  timestamp = {2020-11-30T07:31:31Z}
}

@article{tan_comparative_2014,
  title = {A Comparative Investigation of Non-Linear Activation Functions in Neural Controllers for Search-Based Game {{AI}} Engineering},
  author = {Tan, Tse Guan and Teo, Jason and Anthony, Patricia},
  year = {2014},
  month = jan,
  volume = {41},
  pages = {1--25},
  issn = {1573-7462},
  doi = {10.1007/s10462-011-9294-y},
  abstract = {The creation of intelligent video game controllers has recently become one of the greatest challenges in game artificial intelligence research, and it is arguably one of the fastest-growing areas in game design and development. The learning process, a very important feature of intelligent methods, is the result of an intelligent game controller to determine and control the game objects behaviors' or actions autonomously. Our approach is to use a more efficient learning model in the form of artificial neural networks for training the controllers. We propose a Hill-Climbing Neural Network (HillClimbNet) that controls the movement of the Ms. Pac-man agent to travel around the maze, gobble all of the pills and escape from the ghosts in the maze. HillClimbNet combines the hill-climbing strategy with a simple, feed-forward artificial neural network architecture. The aim of this study is to analyze the performance of various activation functions for the purpose of generating neural-based controllers to play a video game. Each non-linear activation function is applied identically for all the nodes in the network, namely log-sigmoid, logarithmic, hyperbolic tangent-sigmoid and Gaussian. In general, the results shows an optimum configuration is achieved by using log-sigmoid, while Gaussian is the worst activation function.},
  journal = {Artificial Intelligence Review},
  language = {en},
  number = {1},
  timestamp = {2019-05-06T19:56:04Z}
}

@article{taylorForecastingScale2018,
  title = {Forecasting at {{Scale}}},
  author = {Taylor, Sean J. and Letham, Benjamin},
  year = {2018},
  month = jan,
  volume = {72},
  pages = {37--45},
  publisher = {{Taylor \& Francis}},
  issn = {0003-1305},
  doi = {10.1080/00031305.2017.1380080},
  abstract = {Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high-quality forecasts\textemdash especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting ``at scale'' that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.},
  annotation = {\_eprint: https://doi.org/10.1080/00031305.2017.1380080},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\EK9U7R6Z\\00031305.2017.html:text/html},
  journal = {The American Statistician},
  number = {1},
  timestamp = {2020-11-29T12:48:05Z}
}

@article{timmonsApproximatingActivationFunctions2020,
  title = {Approximating {{Activation Functions}}},
  author = {Timmons, Nicholas Gerard and Rice, Andrew},
  year = {2020},
  month = jan,
  url = {http://arxiv.org/abs/2001.06370},
  urldate = {2020-07-06},
  abstract = {ReLU is widely seen as the default choice for activation functions in neural networks. However, there are cases where more complicated functions are required. In particular, recurrent neural networks (such as LSTMs) make extensive use of both hyperbolic tangent and sigmoid functions. These functions are expensive to compute. We used function approximation techniques to develop replacements for these functions and evaluated them empirically on three popular network configurations. We find safe approximations that yield a 10\% to 37\% improvement in training times on the CPU. These approximations were suitable for all cases we considered and we believe are appropriate replacements for all networks using these activation functions. We also develop ranged approximations which only apply in some cases due to restrictions on their input domain. Our ranged approximations yield a performance improvement of 20\% to 53\% in network training time. Our functions also match or considerably out perform the ad-hoc approximations used in Theano and the implementation of Word2Vec.},
  archivePrefix = {arXiv},
  eprint = {2001.06370},
  eprinttype = {arxiv},
  file = {arXiv Fulltext PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\AAGWRU9P\\Timmons and Rice - 2020 - Approximating Activation Functions.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\3W6KVZ7B\\2001.html:text/html},
  journal = {arXiv:2001.06370 [cs, stat]},
  primaryClass = {cs, stat},
  timestamp = {2020-07-06T04:57:40Z}
}

@article{udellMaximizingSumSigmoids2013,
  title = {Maximizing a Sum of Sigmoids},
  author = {Udell, Madeleine and Boyd, Stephen},
  year = {2013},
  pages = {1--25},
  publisher = {{Citeseer}},
  file = {Full Text:C\:\\Users\\SomefunAgba\\Zotero\\storage\\S3KXSR3L\\Udell and Boyd - 2013 - Maximizing a sum of sigmoids.pdf:application/pdf},
  journal = {Optimization and Engineering},
  timestamp = {2020-11-29T11:29:12Z}
}

@article{uedaImprovementSignaltonoiseRatio2010,
  title = {Improvement of Signal-to-Noise Ratio by Stochastic Resonance in Sigmoid Function Threshold Systems, Demonstrated Using a {{CMOS}} Inverter},
  author = {Ueda, Michihito},
  year = {2010},
  volume = {389},
  pages = {1978--1985},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\XULB5W5V\\S0378437110000890.html:text/html},
  journal = {Physica A: Statistical Mechanics and its Applications},
  number = {10},
  timestamp = {2019-08-04T03:08:36Z}
}

@book{vasArtificialIntelligencebasedElectricalMachines1999,
  title = {Artificial-{{Intelligence}}-Based {{Electrical Machines}} and {{Drives}}: {{Application}} of {{Fuzzy}}, {{Neural}}, {{Fuzzy}}-Neural, and {{Genetic}}-Algorithm-Based {{Techniques}}},
  shorttitle = {Artificial-{{Intelligence}}-Based {{Electrical Machines}} and {{Drives}}},
  author = {Vas, Peter},
  year = {1999},
  month = jan,
  publisher = {{Oxford University Press}},
  address = {{Oxford, New York}},
  abstract = {Recently artificial-intelligence-based techniques (fuzzy logic, neural networks, fuzzy-neural networks, genetic algorithms, etc) have received increased attention world-wide and at present two industrial drives incorporate some form of artificial intelligence. This is the first comprehensive book which discusses numerous AI applications to electrical machines and drives. The drives considered are: d.c. drives, induction motor drives, synchronous motor drives, and switched reluctance motor drives. Sensorless drives are also considered. It is essential reading for anyone interested in acquiring a solid background in AI-based electrical machines and drives. It presents a detailed and unified mathematical and physical treatment.},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\VV528QBB\\artificial-intelligence-based-electrical-machines-and-drives-9780198593973.html:text/html},
  isbn = {978-0-19-859397-3},
  series = {Monographs in {{Electrical}} and {{Electronic Engineering}}},
  timestamp = {2019-08-04T03:15:24Z}
}

@book{waadelandContinuedFractions2008,
  title = {Continued {{Fractions}}},
  author = {Waadeland, Haakon and Lorentzen, Lisa},
  year = {2008},
  publisher = {{Atlantis Press}},
  url = {https://www.springer.com/gb/book/9789491216374},
  urldate = {2019-05-11},
  abstract = {Continued Fractions consists of two volumes \textemdash{} Volume 1: Convergence Theory; and Volume 2: Representation of Functions (tentative title), which is expected in 2011. Volume 1 is dedicated to the convergence and computation of continued fractions, while Volume 2 will treat representations of meromorphic functions by continued fractions. Taken together, the two volumes will present the basic continued fractions theory without requiring too much previous knowledge; some basic knowledge of complex functions will suffice. Both new and advanced graduate students of continued fractions shall get a comprehensive understanding of how these infinite structures work in a number of applications, and why they work so well. A varied buffet of possible applications to whet the appetite is presented first, before the more basic but modernized theory is given. This new edition is the result of an increasing interest in computing special functions by means of continued fractions. The methods described in detail are, in many cases, very simple, yet reliable and efficient.},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\C4X7WBKZ\\9789491216374.html:text/html},
  isbn = {978-94-91216-37-4},
  language = {en},
  series = {Atlantis {{Studies}} in {{Mathematics}} for {{Engineering}} and {{Science}}},
  timestamp = {2019-08-04T06:19:56Z}
}

@article{weiTwosampleDvoretzkyKiefer2012,
  title = {Two-Sample {{Dvoretzky}}\textendash{{Kiefer}}\textendash{{Wolfowitz}} Inequalities},
  author = {Wei, Fan and Dudley, Richard M.},
  year = {2012},
  month = mar,
  volume = {82},
  pages = {636--644},
  issn = {0167-7152},
  doi = {10.1016/j.spl.2011.11.012},
  abstract = {The Dvoretzky\textendash Kiefer\textendash Wolfowitz (DKW) inequality says that if Fn is an empirical distribution function for variables i.i.d.~with a distribution function F, and Kn is the Kolmogorov statistic nsupx|(Fn-F)(x)|, then there is a constant C such that for any M{$>$}0, Pr(Kn{$>$}M){$\leq$}Cexp(-2M2). Massart proved that one can take C=2 (DKWM inequality), which is sharp for F continuous. We consider the analogous Kolmogorov\textendash Smirnov statistic for the two-sample case and show that for m=n, the DKW inequality holds for n{$\geq$}n0 for some C depending on n0, with C=2 if and only if n0{$\geq$}458. The DKWM inequality fails for the three pairs (m,n) with 1{$\leq$}m},
  file = {ScienceDirect Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\NBP9XI4J\\S0167715211003658.html:text/html},
  journal = {Statistics \& Probability Letters},
  language = {en},
  number = {3},
  timestamp = {2020-12-15T21:33:35Z}
}

@article{werbosBackpropagationTimeWhat1990,
  title = {Backpropagation through Time: What It Does and How to Do It},
  shorttitle = {Backpropagation through Time},
  author = {Werbos, P.J.},
  year = {1990},
  month = oct,
  volume = {78},
  pages = {1550--1560},
  issn = {1558-2256},
  doi = {10.1109/5.58337},
  abstract = {Basic backpropagation, which is a simple method now being widely used in areas like pattern recognition and fault diagnosis, is reviewed. The basic equations for backpropagation through time, and applications to areas like pattern recognition involving dynamic systems, systems identification, and control are discussed. Further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations, or true recurrent networks, and other practical issues arising with the method are described. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed. The focus is on designing a simpler version of backpropagation which can be translated into computer code and applied directly by neutral network users.{$<>$}},
  file = {Submitted Version:C\:\\Users\\SomefunAgba\\Zotero\\storage\\RSP75T33\\Werbos - 1990 - Backpropagation through time what it does and how.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\SomefunAgba\\Zotero\\storage\\Z7PF57UQ\\58337.html:text/html},
  journal = {Proceedings of the IEEE},
  number = {10},
  timestamp = {2020-07-09T07:13:32Z}
}

@misc{WHOCoronavirusDisease2020,
  title = {{{WHO Coronavirus Disease}} ({{COVID}}-19) {{Dashboard}}},
  year = {2020},
  url = {https://covid19.who.int},
  urldate = {2020-10-21},
  abstract = {World Health Organization Coronavirus disease situation dashboard presents official daily counts of COVID-19 cases and deaths worldwide, while providing a hub to other resources. Interactive tools, including maps, epidemic curves and other charts and graphics, with downloadable data, allow users to track and explore the latest trends, numbers and statistics at global, regional and country levels.},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\PW2997UU\\covid19.who.int.html:text/html},
  journal = {World Health Organization},
  language = {en},
  timestamp = {2020-10-21T21:58:52Z}
}

@article{wuGeneralizedLogisticGrowth2020,
  title = {Generalized Logistic Growth Modeling of the {{COVID}}-19 Outbreak: Comparing the Dynamics in the 29 Provinces in {{China}} and in the Rest of the World},
  shorttitle = {Generalized Logistic Growth Modeling of the {{COVID}}-19 Outbreak},
  author = {Wu, Ke and Darcet, Didier and Wang, Qian and Sornette, Didier},
  year = {2020},
  month = aug,
  volume = {101},
  pages = {1561--1581},
  issn = {1573-269X},
  doi = {10.1007/s11071-020-05862-6},
  abstract = {Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.},
  file = {Springer Full Text PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\IYKB3SLV\\Wu et al. - 2020 - Generalized logistic growth modeling of the COVID-.pdf:application/pdf},
  journal = {Nonlinear Dynamics},
  language = {en},
  number = {3},
  timestamp = {2020-10-19T05:45:16Z}
}

@inproceedings{wuraola_sqnl_2018,
  title = {{{SQNL}}: {{A New Computationally Efficient Activation Function}}},
  shorttitle = {{{SQNL}}},
  booktitle = {2018 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
  author = {Wuraola, A. and Patel, N.},
  year = {2018},
  month = jul,
  pages = {1--7},
  doi = {10.1109/IJCNN.2018.8489043},
  abstract = {A new activation function is proposed. This activation function uses the square operator to introduce the required non-linearity as compared with the use of an exponential term in the popular TanSig. Smaller computational operation count characterizes the proposed activation function. The key to the effectiveness of this function is a faster convergence when used in Multilayer Perceptron Artificial Neural Network architectural problems. Besides, the derivative of the function is linear, resulting in a quicker gradient computation. The effectiveness and efficiency of the proposed activation function have been compared with the TanSig and the computationally efficient ElliotSig functions using selected UCI datasets. The MNIST handwritten dataset was also used to show the ability of this function on more massive datasets. An empirical comparison suggests that the proposed function outperforms TanSig and ElliotSig in convergence time as well as in generalization metrics for most datasets.},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\SomefunAgba\\Zotero\\storage\\AD2B85GD\\8489043.html:text/html},
  timestamp = {2019-05-06T20:05:17Z}
}

@article{xsRichardsModelRevisited2012,
  title = {Richards Model Revisited: Validation by and Application to Infection Dynamics},
  shorttitle = {Richards Model Revisited},
  author = {Xs, Wang and J, Wu and Y, Yang},
  year = {2012},
  month = nov,
  volume = {313},
  publisher = {{J Theor Biol}},
  issn = {1095-8541},
  doi = {10.1016/j.jtbi.2012.07.024},
  abstract = {Ever since Richards proposed his flexible growth function more than half a century ago, it has been a mystery that this empirical function has made many incredible coincidences with real ecological or epidemic data even though one of its parameters (i.e., the exponential term) does not seem to have \ldots},
  file = {Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\S76JDWH5\\22889641.html:text/html},
  journal = {Journal of theoretical biology},
  language = {en},
  pmid = {22889641},
  timestamp = {2020-10-22T10:01:17Z}
}

@article{yasienChatteringAttenuationSliding2009,
  title = {Chattering {{Attenuation}} of {{Sliding Mode Controller Using Genetic Algorithm}} and {{Fuzzy Logic Techniques}}},
  author = {Yasien, Farzdaq R. and Kadhim, Mina Q.},
  year = {2009},
  volume = {27},
  pages = {2595--2610},
  file = {Full Text:C\:\\Users\\SomefunAgba\\Zotero\\storage\\9FQAJCGY\\Yasien and Kadhim - 2009 - Chattering Attenuation of Sliding Mode Controller .pdf:application/pdf;Snapshot:C\:\\Users\\SomefunAgba\\Zotero\\storage\\7I3CD97D\\iasj.html:text/html},
  journal = {Engineering and Technology Journal},
  number = {14},
  timestamp = {2019-08-03T04:43:54Z}
}

@inproceedings{yeoModellingTechniqueUtilizing2016,
  title = {Modelling Technique Utilizing Modified Sigmoid Functions for Describing Power Transistor Device Capacitances Applied on {{GaN HEMT}} and Silicon {{MOSFET}}},
  booktitle = {2016 {{IEEE Applied Power Electronics Conference}} and {{Exposition}} ({{APEC}})},
  author = {Yeo, H. L. and Tseng, K. J.},
  year = {2016},
  month = mar,
  pages = {3107--3114},
  doi = {10.1109/APEC.2016.7468308},
  abstract = {In power transistor models, it is very important that device capacitances are modelled accurately so that switching losses, EMI filter requirements and gate timing requirements of the converter can be accurately determined. In this paper, a modelling technique utilizing modified sigmoid functions to describe the device capacitances is applied on a GaN HEMT and a silicon MOSFET to develop their corresponding SPICE models. Comparison of switching energies of the transistors under slow-switching conditions suggest that the technique is suitable for modelling both types of transistors. The developed model for the GaN HEMT is also shown to simulate faster than the manufacturer's model under fast switching conditions.},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\SomefunAgba\\Zotero\\storage\\JVJSI8ZZ\\7468308.html:text/html},
  timestamp = {2019-08-03T05:40:13Z}
}

@article{yinFlexibleSigmoidFunction2003,
  title = {A {{Flexible Sigmoid Function}} of {{Determinate Growth}}},
  author = {Yin, Xinyou and Goudriaan, Jan and Lantinga, Egbert A. and Vos, Jan and Spiertz, Huub J.},
  year = {2003},
  month = feb,
  volume = {91},
  pages = {361--371},
  issn = {0305-7364},
  doi = {10.1093/aob/mcg029},
  abstract = {A new empirical equation for the sigmoid pattern of determinate growth, `the beta growth function', is presented. It calculates weight (w) in dependence of time, using the following three parameters: tm, the time at which the maximum growth rate is obtained; te, the time at the end of growth; and wmax, the maximal value for w, which is achieved at te. The beta growth function was compared with four classical (logistic, Richards, Gompertz and Weibull) growth equations, and two expolinear equations. All equations described successfully the sigmoid dynamics of seed filling, plant growth and crop biomass production. However, differences were found in estimating wmax. Features of the beta function are: (1) like the Richards equation it is flexible in describing various asymmetrical sigmoid patterns (its symmetrical form is a cubic polynomial); (2) like the logistic and the Gompertz equations its parameters are numerically stable in statistical estimation; (3) like the Weibull function it predicts zero mass at time zero, but its extension to deal with various initial conditions can be easily obtained; (4) relative to the truncated expolinear equation it provides more reasonable estimates of final quantity and duration of a growth process. In addition, the new function predicts a zero growth rate at both the start and end of a precisely defined growth period. Therefore, it is unique for dealing with determinate growth, and is more suitable than other functions for embedding in process-based crop simulation models to describe the dynamics of organs as sinks to absorb assimilates. Because its parameters correspond to growth traits of interest to crop scientists, the beta growth function is suitable for characterization of environmental and genotypic influences on growth processes. However, it is not suitable for estimating maximum relative growth rate to characterize early growth that is expected to be close to exponential.},
  file = {PubMed Central Full Text PDF:C\:\\Users\\SomefunAgba\\Zotero\\storage\\ZNSNM4NC\\YIN et al. - 2003 - A Flexible Sigmoid Function of Determinate Growth.pdf:application/pdf},
  journal = {Annals of Botany},
  number = {3},
  pmcid = {PMC4244967},
  pmid = {12547689},
  timestamp = {2020-11-29T12:24:59Z}
}

@article{zamanlooy_efficient_2014,
  title = {Efficient {{VLSI Implementation}} of {{Neural Networks With Hyperbolic Tangent Activation Function}}},
  author = {Zamanlooy, B. and Mirhassani, M.},
  year = {2014},
  month = jan,
  volume = {22},
  pages = {39--48},
  issn = {1063-8210},
  doi = {10.1109/TVLSI.2012.2232321},
  abstract = {Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions. Accurate implementation of these transfer functions in digital networks faces certain challenges. In this paper, an efficient approximation scheme for hyperbolic tangent function is proposed. The approximation is based on a mathematical analysis considering the maximum allowable error as design parameter. Hardware implementation of the proposed approximation scheme is presented, which shows that the proposed structure compares favorably with previous architectures in terms of area and delay. The proposed structure requires less output bits for the same maximum allowable error when compared to the state-of-the-art. The number of output bits of the activation function determines the bit width of multipliers and adders in the network. Therefore, the proposed activation function results in reduction in area, delay, and power in VLSI implementation of artificial neural networks with hyperbolic tangent activation function.},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\SomefunAgba\\Zotero\\storage\\5F3Q3EGD\\6409494.html:text/html},
  journal = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems},
  number = {1},
  timestamp = {2019-05-06T20:10:56Z}
}

license_type: permissive

branch_name: refs/heads/main

extension: bib


text:
// Copyright 2018 gopcua authors. All rights reserved.
// Use of this source code is governed by a MIT-style license that can be
// found in the LICENSE file.

package uasc

import (
	"encoding/hex"
	"testing"
)

var testSymmetricSecurityHeaderBytes = [][]byte{
	{
		// TokenID
		0x44, 0x33, 0x22, 0x11,
		// dummy Payload
		0xde, 0xad, 0xbe, 0xef,
	},
	{},
	{},
}

func TestDecodeSymmetricSecurityHeader(t *testing.T) {
	s, err := DecodeSymmetricSecurityHeader(testSymmetricSecurityHeaderBytes[0])
	if err != nil {
		t.Fatalf("Failed to decode SymmetricSecurityHeader: %s", err)
	}

	dummyStr := hex.EncodeToString(s.Payload)
	switch {
	case s.TokenID != 0x11223344:
		t.Errorf("TokenID doesn't match. Want: %x, Got: %x", 0x11223344, s.TokenID)
	case dummyStr != "deadbeef":
		t.Errorf("Payload doesn't match. Want: %s, Got: %s", "deadbeef", dummyStr)
	}
	t.Log(s.String())
}

func TestSerializeSymmetricSecurityHeader(t *testing.T) {
	a := NewSymmetricSecurityHeader(
		0x11223344,
		[]byte{0xde, 0xad, 0xbe, 0xef},
	)

	serialized, err := a.Serialize()
	if err != nil {
		t.Fatalf("Failed to serialize Header: %s", err)
	}

	for i, s := range serialized {
		x := testSymmetricSecurityHeaderBytes[0][i]
		if s != x {
			t.Errorf("Bytes doesn't match. Want: %#x, Got: %#x at %dth", x, s, i)
		}
	}
	t.Logf("%x", serialized)
}

license: MIT

branch_name: refs/heads/master

extension: go


text:
DESCRIPTION = "Store knowledge in form of questions and answers and train them."
HOMEPAGE = "http://code.google.com/p/ebrainy/"
AUTHOR = "quickdev"
LICENSE = "GPL"
SECTION = "e/apps"
RDEPENDS_${PN} = "elementary sqlite3 python-sqlalchemy python-mysqldb python-netserver"

PV = "0.0.1+svnr${SRCPV}"
PR = "r7"
SRCREV = "21"
SRC_URI = "svn://ebrainy.googlecode.com/svn/trunk;module=.;proto=http"
S = "${WORKDIR}"

inherit distutils
FILES_${PN} += "${datadir}/applications/ebrainy.desktop"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
@article{Bahrami-2021-Data,
    title = "Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model",
    author = {Bahrami, Ala  and
      Go{\"\i}ta, Kalifa  and
      Magagi, Ramata  and
      Davison, Bruce  and
      Razavi, Saman  and
      Elshamy, Mohamed  and
      Princz, Daniel},
    journal = "Journal of Hydrology, Volume 597",
    volume = "597",
    year = "2021",
    publisher = "Elsevier BV",
    url = "https://aclanthology.org/G21-27001",
    doi = "10.1016/j.jhydrol.2020.125744",
    pages = "125744",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

gha_language: Python


text:
package log

import (
	"os"
	"time"
	"fmt"
)

var printNormal=true
var printDebug=false
var WTF=false
var logFile *os.File
//StartLog 开启日志
//mode=1 显示普通日志，不显示详细日志，不写入到文件
//mode=2 显示普通日志，显示详细日志，不写入到文件
//mode=3 显示普通日志，不显示详细日志，写入所有日志到文件
//mode=4 显示普通日志，显示详细日志，写入所有日志到文件
func StartLog(mode int){
	os.Mkdir("logfile",0666)
	if mode==1{
	}else if mode==2{
		printDebug=true
	}else if mode==3{
		WTF=true
	}else if mode==4{
		printDebug=true
		WTF=true
	}
	if WTF{
		f,err:=os.OpenFile("logfile/"+time.Now().Format("20060102150405.999.txt"),os.O_CREATE|os.O_APPEND,0666)
		if err!=nil{
			WTF=false
			NPrint("log",err.Error())
		}else{
			logFile=f
		}
	}
}
func NPrint(where string,msg string){
	t:=fmt.Sprintln(time.Now().Format("2006-01-02 15:04:05.999N"),fmt.Sprintf("[%s] %s",where,msg))
	fmt.Print(t)
	if WTF{
		logFile.WriteString(t)
	}
}
func DPrint(where string,msg string){
	if !printDebug{
		return
	}
	t:=fmt.Sprintln(time.Now().Format("2006-01-02 15:04:05.999V"),fmt.Sprintf("[%s] %s",where,msg))
	fmt.Print(t)
	if WTF{
		logFile.WriteString(t)
	}
}

repository: crabkun/crabftp

repo_name: crabkun/crabftp


text:
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=ab6706baf6d39a6b0fa2613a3b0831e7"

DEPENDS = "lua"

RDEPENDS:${PN} += "lua"

SRC_URI = "git://github.com/diegonehab/luasocket;protocol=https;branch=master \
	file://0001-fix-for-OE.patch \
"

# Modify these as desired
PV = "0.0+git${SRCPV}"
SRCREV = "652959890943c34d7180cae372339b91e62f0d7b"

S = "${WORKDIR}/git"

FILES:${PN} = "${libdir} ${datadir}/lua"

EXTRA_OEMAKE = 'DESTDIR=${D} BASELIB=${base_libdir} PREFIX=/usr CC="${CC}" LD="${CC}" MYLDFLAGS="${LDFLAGS}"'

inherit pkgconfig

do_configure () {
}

do_compile () {
	# You will almost certainly need to add additional arguments here
	oe_runmake
}

do_install () {
	# This is a guess; additional arguments may be required
	oe_runmake install
}

repository: YoeDistro/meta-swupdate

repo_name: YoeDistro/meta-swupdate

gha_license_id: MIT


text:
SUMMARY = "Test automation framework with Expect style execution of commands"
DESCRIPTION = "Lux (LUcid eXpect scripting) is a test automation framework with Expect style execution of commands. See [Expect][] for more info about the origin."

LICENSE = "Apache-2.0"
LIC_FILES_CHKSUM = "file://LICENSE;md5=c6aa54a157ba92156983bf01e1eb7728"

SRC_URI = "git://github.com/hawk/lux;protocol=https;branch=master"

PV = "2.6+git${SRCPV}"
SRCREV = "ec04d30d335bc522eb67880035d1d5de2013e34f"

S = "${WORKDIR}/git"

DEPENDS = "erlang-native"
RDEPENDS:${PN} = "erlang"

inherit autotools-brokensep

EXTRA_OECONF = ""

BBCLASSEXTEND = "nativesdk"

repository: adrians/meta-erlang

repo_name: adrians/meta-erlang


text:
SUMMARY = "AVR udev rules allow unpriviledged users acces to AVR dev tools"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/COPYING.MIT;md5=3da9cfbcb788c80a0384361b4de20420"

inherit allarch

SRC_URI = "file://60-avr-dev-devices.rules"
PV = "0.1"

do_install () {
    install -d ${D}${sysconfdir}/udev/rules.d
    install -m 0644 ${WORKDIR}/60-avr-dev-devices.rules ${D}${sysconfdir}/udev/rules.d/
}

repository: pschneiders/meta-microcontroller

repo_name: pschneiders/meta-microcontroller


text:
package utils

import (
	"io/ioutil"
	"strings"
)

func StripConf(path string) (lines []string, err error) {
	tmp, err := ioutil.ReadFile(path)
	if err != nil {
		return
	}
	tmp_lines := strings.Split(string(tmp), "\n")
	lines = make([]string, 0)
	for _, line := range tmp_lines {
		line = strings.TrimSpace(line)
		if len(line) == 0 || line[0] == '#' {
			continue
		}
		lines = append(lines, line)
	}
	return
}

func FetchConfKey(lines []string, target, def string) string {
	find := def
	for _, line := range lines {
		if strings.Fields(line)[0] == target {
			find = line
		}
	}
	return find
}

func FetchConfBlock(lines []string, begin, end string) []string {
	var hit bool
	var res []string
	for _, line := range lines {
		if hit {
			if strings.Fields(line)[0] == end {
				hit = false
			}
			res = append(res, line)
		} else {
			if strings.Fields(line)[0] == begin {
				hit = true
				res = []string{line}
			}
		}
	}
	return res
}

repository: CJey/oum

repo_name: CJey/oum


text:
//
// Copyright (c) 2017. Marcus Brummer.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//

package gopixl

/*
#cgo LDFLAGS: -lpixl
#include <pixl/pixl.h>
#include <stdlib.h>
*/
import "C"
import "unsafe"

// Image todo
type Image struct {
	Width  uint32
	Height uint32
	handle *C.CPixlImage
}

// LoadImage loads the image at the specified path
func LoadImage(path string) *Image {
	cpath := C.CString(path)
	img := C.pixl_load_image(cpath)
	C.free(unsafe.Pointer(cpath))

	return &Image{
		Width:  uint32(img.width),
		Height: uint32(img.height),
		handle: img,
	}
}

// Save saves image
func (img *Image) Save(path string, quality int) {
	cpath := C.CString(path)
	C.pixl_save_image(img.handle, cpath, C.int(quality))
	C.free(unsafe.Pointer(cpath))
}

// Free frees resources
func (img *Image) Free() {
	C.pixl_destroy_image(img.handle)
}

repository: mbrlabs/gopixl

repo_name: mbrlabs/gopixl


text:
package cqs

import (
	"context"

	"github.com/go-redis/cache/v9"
	"github.com/shortlink-org/shortlink/internal/pkg/logger"
	v1 "github.com/shortlink-org/shortlink/internal/services/link/domain/link/v1"
	v12 "github.com/shortlink-org/shortlink/internal/services/metadata/domain/metadata/v1"
)

type Repository interface {
	LinkAdd(ctx context.Context, data *v1.Link) (*v1.Link, error)
	LinkUpdate(ctx context.Context, data *v1.Link) (*v1.Link, error)
	LinkDelete(ctx context.Context, id string) error

	MetadataUpdate(ctx context.Context, data *v12.Meta) (*v12.Meta, error)
}

// Store abstract type
type Store struct {
	log       logger.Logger
	store     Repository
	cache     *cache.Cache
	typeStore string
}

repository: shortlink-org/shortlink

repo_name: shortlink-org/shortlink

gha_language: Go


text:
inherit autotools

DESCRIPTION = "Procinfo displays various system statistics including: memory \
used, free, swapped out; CPU time used for user applications, kernel \
(system), nice applications, idle; IRQs per second; page-in/out and \
swap-in/out per second, and disc activity."
HOMEPAGE = "http://sourceforge.net/projects/procinfo-ng/"
LICENSE = "LGPLv2.1 GPLv2"
DEPENDS = "ncurses"
PROVIDES += "procinfo"
RPROVIDES_${PN} += "procinfo"
PR = "r0"

SRC_URI = "${SOURCEFORGE_MIRROR}/procinfo-ng/procinfo-ng-${PV}.tar.bz2 \
           file://obey-ldflags.patch"
SRC_URI[md5sum] = "ed018fe37becc36ceb1ebe3ed909c4d2"
SRC_URI[sha256sum] = "0cd944df90c1ea55b489ea64ec4863fe7f1a158f16660c58f94067e4f96786b0"


text:
DESCRIPTION = "Qt/Embedded version ${PV}"
SECTION = "libs"
PRIORITY = "optional"
LICENSE = "GPL QPL"
DEPENDS = "zlib libpng jpeg tslib qmake-native-1.08a uicmoc4-native"
PROVIDES = "virtual/qte4 virtual/libqte4"
MAINTAINER = "Michael 'Mickey' Lauer <mickey@Vanille.de>"
PR = "ml1"

BROKEN = "1"

PV = "4.0.0-b2-snapshot-20050128"

FILESDIR = "${@os.path.dirname(bb.data.getVar('FILE',d,1))}/qte-4.0.0-snapshot"

SRC_URI = "ftp://ftp.trolltech.com/pub/qt/snapshots/qt-embedded-opensource-${PV}.tar.bz2 \
           file://gcc34.patch;patch=1 \
           file://add-qatomic.patch;patch=1 \
           file://fix-mkspecs.patch;patch=1 \
           file://fix-qwsmanager.patch;patch=1 \
           file://fix-sl5000driver.patch;patch=1 \
           file://no-moc-no-uic-no-rcc.patch;patch=1 \
           file://sharp_char.h \
           file://switches.h \
           file://update-qtfontdir"
S = "${WORKDIR}/qt-embedded-opensource-${PV}"

DEFAULT_PREFERENCE = "-1"

export QTDIR = "${S}"

def qte_arch(d):
	import bb, re
	arch = bb.data.getVar('TARGET_ARCH', d, 1)
	if re.match("^i.86$", arch):
		arch = "x86"
	elif arch == "x86_64":
		arch = "x86"
	elif arch == "mipsel":
		arch = "mips"
	return arch

QTE_ARCH := "${@qte_arch(d)}"

#
# How to build the embedded drivers. Use plugin-<type> or qt-<type>. As for Beta1, that doesn't work :D
#
GFX = "qt-gfx"
KBD = "qt-kbd"
MSE = "qt-mouse"

#
# Which configuration to build. As for Beta1, this doesn't work :)
#
QCONFIG = ""
# QCONFIG = "-qconfig full"
QDEPTHS = "-depths 8,16,24,32"

#
# Borken: yopy, busmouse, linuxtp
#

EXTRA_OECONF = "-embedded ${QTE_ARCH} \
		-system-libjpeg -system-libpng -system-zlib \
		-no-qvfb -no-nis -no-cups -no-stl -no-pch   \
		-no-accessibility -no-compat -fast          \
		${QCONFIG} ${QDEPTHS}                       \
                -qt-gfx-transformed			    \
		-qt-gfx-vnc             		    \
                -${KBD}-sl5000                          \
		-${KBD}-tty                             \
		-${KBD}-usb                             \
		-${MSE}-pc                            \
		"

EXTRA_OEMAKE = "-e"
PARALLEL_MAKE = ""

export EXTRA_CFLAGS = "-I${STAGING_INCDIR}"
export EXTRA_CXXFLAGS = "-I${STAGING_INCDIR}"
export EXTRA_LFLAGS = "-L${STAGING_LIBDIR} -Wl,-rpath-link,${STAGING_LIBDIR}"

do_configure() {
	echo "DEFINES -= QT_NO_CAST_TO_ASCII" >>src/qbase.pri
	unset QMAKESPEC
	echo yes | ./configure ${EXTRA_OECONF} || die "Configuring qt failed. EXTRA_OECONF was ${EXTRA_OECONF}"
}

do_compile() {
        unset CC LD CCLD CXX RANLIB AR STRIP CFLAGS LDFLAGS CXXFLAGS CPPFLAGS LINK
	install -m 0755 ${STAGING_BINDIR}/rcc4 ${S}/bin/rcc
	install -m 0755 ${STAGING_BINDIR}/moc4 ${S}/bin/moc
	install -m 0755 ${STAGING_BINDIR}/uic4 ${S}/bin/uic

	install -d include/asm/
	install -m 0644 ${WORKDIR}/sharp_char.h include/asm/
	install -d include/linux/
	install -m 0644 ${WORKDIR}/switches.h   include/linux/

	oe_runmake
}

do_stage() {
	install -d ${STAGING_DIR}/${HOST_SYS}/qt4/lib

        for lib in Core Gui Network Sql Xml
        do
                cp -pPR lib/libQt${lib}* ${STAGING_DIR}/${HOST_SYS}/qt4/lib/
        done

	install -d ${STAGING_DIR}/${HOST_SYS}/qt4/include/
	cp -pPR include/* ${STAGING_DIR}/${HOST_SYS}/qt4/include
}

do_install() {
	install -d ${D}${palmtopdir}/bin
	install -d ${D}${sbindir}/
	install -m 0755 ${WORKDIR}/update-qtfontdir ${D}${sbindir}/
	install -d ${D}${palmtopdir}/lib/fonts/
	cp -pPR lib/fonts/* ${D}${palmtopdir}/lib/fonts/

	for lib in Core Gui Network Sql Xml
	do
		oe_soinstall lib/libQt${lib}.so.4.0.0 ${D}${palmtopdir}/lib
	done

	for i in `find . -perm 0755 -type f`
	do
		install -m 0755 $i ${D}${palmtopdir}/bin/`basename $i`
	done
}

pkg_postinst() {
#!/bin/sh
if [ -n "$D" ]; then exit 1; fi
set -e
. /etc/profile
${sbindir}/update-qtfontdir
}

pkg_postinst_qte-font-unicode() {
#!/bin/sh
if [ -n "$D" ]; then exit 1; fi
set -e
. /etc/profile
${sbindir}/update-qtfontdir
}

pkg_postinst_qte-font-lcd () {
#!/bin/sh
if [ -n "$D" ]; then exit 1; fi
set -e
. /etc/profile
${sbindir}/update-qtfontdir
}

pkg_postinst_qte-font-japanese() {
#!/bin/sh
if [ -n "$D" ]; then exit 1; fi
set -e
. /etc/profile
${sbindir}/update-qtfontdir
}

pkg_postinst_qte-font-micro() {
#!/bin/sh
if [ -n "$D" ]; then exit 1; fi
set -e
. /etc/profile
${sbindir}/update-qtfontdir
}

pkg_postinst_qte-font-courier() {
#!/bin/sh
if [ -n "$D" ]; then exit 1; fi
set -e
. /etc/profile
${sbindir}/update-qtfontdir
}

LIB_PACKAGES = "\
		libqte4-debug			\
		libqte4-core			\
		libqte4-gui			\
		libqte4-network			\
		libqte4-sql			\
		libqte4-xml			\
		"

FONT_PACKAGES = "\
		qte-font-fixed			\
		qte-font-helvetica-small	\
		qte-font-helvetica-large 	\
		qte-font-smoothtimes 		\
		qte-font-smallsmooth		\
		qte-font-unicode		\
		qte-font-lcd 			\
		qte-font-japanese 		\
		qte-font-micro 			\
		qte-font-courier		\
		"

PACKAGES = "${LIB_PACKAGES} ${FONT_PACKAGES} examples"

PACKAGE_ARCH = "${MACHINE_ARCH}"

FILES_${PN} = ""
FILES_libqte4-debug	= "${palmtopdir}/lib/libQt*_debug.*"
FILES_libqte4-core	= "${palmtopdir}/lib/libQtCore.* ${sbindir}/update-qtfontdir"
FILES_libqte4-gui	= "${palmtopdir}/lib/libQtGui.*"
FILES_libqte4-network	= "${palmtopdir}/lib/libQtNetwork.*"
FILES_libqte4-sql	= "${palmtopdir}/lib/libQtSql.*"
FILES_libqte4-xml	= "${palmtopdir}/lib/libQtXml.*"
FILES_examples		= "${palmtopdir}/bin"

FILES_qte-font-fixed = "${palmtopdir}/lib/fonts/fixed*"
PACKAGE_ARCH_qte-font-fixed = "all"

FILES_qte-font-helvetica-small = "${palmtopdir}/lib/fonts/helvetica_80*.qpf \
	${palmtopdir}/lib/fonts/helvetica_100*.qpf ${palmtopdir}/lib/fonts/helvetica_120*.qpf"
PACKAGE_ARCH_qte-font-helvetica-small = "all"

FILES_qte-font-helvetica-large = "${palmtopdir}/lib/fonts/helvetica_140*.qpf \
	${palmtopdir}/lib/fonts/helvetica_180*.qpf ${palmtopdir}/lib/fonts/helvetica_240*.qpf"
PACKAGE_ARCH_qte-font-helvetica-large = "all"

FILES_qte-font-smoothtimes = "${palmtopdir}/lib/fonts/smoothtimes*"
PACKAGE_ARCH_qte-font-smoothtimes = "all"

FILES_qte-font-smallsmooth = "${palmtopdir}/lib/fonts/smallsmooth*"
PACKAGE_ARCH_qte-font-smallsmooth = "all"

FILES_qte-font-unicode = "${palmtopdir}/lib/fonts/unifont*.qpf"
PACKAGE_ARCH_qte-font-unicode = "all"

FILES_qte-font-lcd = "${palmtopdir}/lib/fonts/lcd*"
PACKAGE_ARCH_qte-font-lcd = "all"

FILES_qte-font-japanese = "${palmtopdir}/lib/fonts/japanese*"
PACKAGE_ARCH_qte-font-japanese = "all"

FILES_qte-font-micro = "${palmtopdir}/lib/fonts/micro*.qpf"
PACKAGE_ARCH_qte-font-micro = "all"

FILES_qte-font-courier = "${palmtopdir}/lib/fonts/cour*"
PACKAGE_ARCH_qte-font-courier = "all"

repository: Terentyev/familiar-linux

repo_name: Terentyev/familiar-linux


text:
package fox

type Statistics struct {
	TimeSinceLastOK 		int64	`json:"timeSinceLastOK"`
	TimeSinceLastNOK 		int64	`json:"timeSinceLastNOK"`
	ParallelRequestCount	int		`json:"currentRequestCount"`
	NodeName				string	`json:"nodeName"`
}

repository: livenson/fox

repo_name: livenson/fox


text:
package main

import (
	"fmt"
	"time"

	"github.com/luoruofeng/NewsFMAPI/api"
)

func main() {
	var (
		err error
	)

	if err = api.InitConfig("./main.json"); err != nil {
		goto ERR
	}

	if err = api.InitApiServer(); err != nil {
		goto ERR
	}

	if err = api.InitArticleMgr(); err != nil {
		goto ERR
	}

	for {
		time.Sleep(2 * time.Second)
	}
ERR:
	fmt.Println(err)
}

repository: luoruofeng/NewsFMAPI

repo_name: luoruofeng/NewsFMAPI


text:
@inproceedings{oliveira2012clonal,
 abstract = {This study proposes a new algorithm for supervised learning, based on the clonal selection principle exhibited in natural and artificial immune systems. The method, called Clonal Selection Classifier with Data Reduction (CSCDR), utilizes a fitness function based on the number of correct and incorrect pattern classifications made by each antibody. The algorithm tries to maximize this value through clonal selection processes such as mutation, affinity maturation and selection of the best individuals, transforming the training phase in an optimization problem. This leads to antibodies with more representativeness and thus decreases the amount of prototypes generated at the end of the algorithm. Experimental results on benchmark datasets of the UCI machine learning repository demonstrated the effectiveness of the CSCDR algorithm as a classification technique, combined with a considerable data reduction when compared to the results obtained by the well known Artificial Immune Recognition System (AIRS) and the original Clonal Selection Classifier Algorithm (CSCA).},
 author = {Luiz Otavio V. B. Oliveira and Mota, Rodrigo Luiz Mendes and Barone, Dante Augusto Couto},
 booktitle = {Evolutionary Computation (CEC), 2012 IEEE Congress on},
 doi = {10.1109/CEC.2012.6256133},
 file = {:Artigos/my-publications/Clonal Selection Classifier with Data Reduction Classification as an Optimization Task.pdf:PDF},
 groups = {Selecao clonal},
 organization = {IEEE},
 owner = {luiz},
 pages = {1--7},
 timestamp = {2017.07.24},
 title = {Clonal selection classifier with data reduction: Classification as an optimization task},
 url = {https://ieeexplore.ieee.org/document/6256133},
 year = {2012}
}

repository: luizvbo/academic-kickstart

repo_name: luizvbo/academic-kickstart


text:
HOMEPAGE = "https://sourceware.org/newlib/"
SUMMARY = "C library for embedded systems"
DESCRIPTION = "Newlib is a conglomeration of several library parts, all under free software licenses that make them easily usable on embedded products."

LICENSE = "GPLv2 & LGPLv3 & GPLv3 & LGPLv2"
LIC_FILES_CHKSUM = "file://COPYING;md5=59530bdf33659b29e73d4adb9f9f6552 \
                    file://COPYING3.LIB;md5=6a6a8e020838b23406c81b19c1d46df6 \
                    file://COPYING3;md5=d32239bcb673463ab874e80d47fae504 \
                    file://COPYING.LIBGLOSS;md5=d15bfccaf2c20f3287951bd3f768db5f \
                    file://COPYING.LIB;md5=9f604d8a4f8e74f4f5140845a21b6674 \
                    file://COPYING.NEWLIB;md5=27039641b800547bbcea82a8a5b707ad \
                    file://newlib/libc/posix/COPYRIGHT;md5=103468ff1982be840fdf4ee9f8b51bbf \
                    file://newlib/libc/sys/linux/linuxthreads/LICENSE;md5=73640207fbc79b198c7ffd4ad4d97aa0"

SRC_URI = "file://newlib-2.2.0.tar.gz"

DEPENDS = "flex-native bison-native m4-native"
DEPENDS_remove = "virtual/libc virtual/${TARGET_PREFIX}compilerlibs"
PACKAGES = "${PN}"

# x86 specific settings
#NEWLIB_HOST_x86 ?= "i586-poky-elf"
TUNE_CCARGS_x86 := " -nostdlib"

# MIPS specific settings
#NEWLIB_HOST_mips ?= "mips-poky-elf"
#TUNE_CCARGS_mips := "-mabi=32 -march=mips32r2 -nostdlib"
TUNE_CCARGS_mips := "-nostdlib"

# ARM specific settings
#TUNE_CCARGS_arm := "-mthumb -mcpu=cortex-m3 -march=armv7-m -nostdlib"
#NEWLIB_HOST_arm ?= "armv5-poky-eabi"
TUNE_CCARGS_arm := "-nostdlib"

# This will determine the name of the folder with libgcc as well.
NEWLIB_HOST = "${TARGET_SYS}"

CFLAGS += " -DMISSING_SYSCALL_NAMES "

# Specify any options you want to pass to the configure script using EXTRA_OECONF:
EXTRA_OECONF = " --enable-languages=c \
    --host=${NEWLIB_HOST} \
    --with-newlib --with-gnu-as --with-gnu-ld -v \
"

do_configure () {
    # If we're being rebuilt due to a dependency change, we need to make sure
    # everything is clean before we configure and build -- if we haven't previously
    # built this will fail and be ignored.
    make distclean || :
    export CC_FOR_TARGET=$CC
    ${S}/configure ${EXTRA_OECONF}
}

do_install () {
    oe_runmake 'DESTDIR=${D}' install

    # Delete standards.info, configure.info
    rm -rf ${D}/usr/share/
}

FILES_${PN} = "/usr/${NEWLIB_HOST}"

INSANE_SKIP_${PN} = "staticdev"

repository: jurobystricky/meta-zephyr

repo_name: jurobystricky/meta-zephyr


text:
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// 	protoc-gen-go v1.23.0
// 	protoc        v3.11.4
// source: chat.proto

package pd

import (
	proto "github.com/golang/protobuf/proto"
	protoreflect "google.golang.org/protobuf/reflect/protoreflect"
	protoimpl "google.golang.org/protobuf/runtime/protoimpl"
	reflect "reflect"
	sync "sync"
)

const (
	// Verify that this generated code is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
	// Verify that runtime/protoimpl is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)

// This is a compile-time assertion that a sufficiently up-to-date version
// of the legacy proto package is being used.
const _ = proto.ProtoPackageIsVersion4

type Chat struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	Id     uint32 `protobuf:"varint,1,opt,name=Id,proto3" json:"Id,omitempty"`        // 消息id
	Type   uint32 `protobuf:"varint,2,opt,name=Type,proto3" json:"Type,omitempty"`    // 消息类别
	Title  string `protobuf:"bytes,3,opt,name=Title,proto3" json:"Title,omitempty"`   // 消息标题
	Text   string `protobuf:"bytes,4,opt,name=Text,proto3" json:"Text,omitempty"`     // 消息内容
	Author string `protobuf:"bytes,5,opt,name=Author,proto3" json:"Author,omitempty"` // 作者
	Send   string `protobuf:"bytes,6,opt,name=Send,proto3" json:"Send,omitempty"`     // 接收者
}

func (x *Chat) Reset() {
	*x = Chat{}
	if protoimpl.UnsafeEnabled {
		mi := &file_chat_proto_msgTypes[0]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *Chat) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*Chat) ProtoMessage() {}

func (x *Chat) ProtoReflect() protoreflect.Message {
	mi := &file_chat_proto_msgTypes[0]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use Chat.ProtoReflect.Descriptor instead.
func (*Chat) Descriptor() ([]byte, []int) {
	return file_chat_proto_rawDescGZIP(), []int{0}
}

func (x *Chat) GetId() uint32 {
	if x != nil {
		return x.Id
	}
	return 0
}

func (x *Chat) GetType() uint32 {
	if x != nil {
		return x.Type
	}
	return 0
}

func (x *Chat) GetTitle() string {
	if x != nil {
		return x.Title
	}
	return ""
}

func (x *Chat) GetText() string {
	if x != nil {
		return x.Text
	}
	return ""
}

func (x *Chat) GetAuthor() string {
	if x != nil {
		return x.Author
	}
	return ""
}

func (x *Chat) GetSend() string {
	if x != nil {
		return x.Send
	}
	return ""
}

var File_chat_proto protoreflect.FileDescriptor

var file_chat_proto_rawDesc = []byte{
	0x0a, 0x0a, 0x63, 0x68, 0x61, 0x74, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x12, 0x02, 0x70, 0x64,
	0x22, 0x80, 0x01, 0x0a, 0x04, 0x43, 0x68, 0x61, 0x74, 0x12, 0x0e, 0x0a, 0x02, 0x49, 0x64, 0x18,
	0x01, 0x20, 0x01, 0x28, 0x0d, 0x52, 0x02, 0x49, 0x64, 0x12, 0x12, 0x0a, 0x04, 0x54, 0x79, 0x70,
	0x65, 0x18, 0x02, 0x20, 0x01, 0x28, 0x0d, 0x52, 0x04, 0x54, 0x79, 0x70, 0x65, 0x12, 0x14, 0x0a,
	0x05, 0x54, 0x69, 0x74, 0x6c, 0x65, 0x18, 0x03, 0x20, 0x01, 0x28, 0x09, 0x52, 0x05, 0x54, 0x69,
	0x74, 0x6c, 0x65, 0x12, 0x12, 0x0a, 0x04, 0x54, 0x65, 0x78, 0x74, 0x18, 0x04, 0x20, 0x01, 0x28,
	0x09, 0x52, 0x04, 0x54, 0x65, 0x78, 0x74, 0x12, 0x16, 0x0a, 0x06, 0x41, 0x75, 0x74, 0x68, 0x6f,
	0x72, 0x18, 0x05, 0x20, 0x01, 0x28, 0x09, 0x52, 0x06, 0x41, 0x75, 0x74, 0x68, 0x6f, 0x72, 0x12,
	0x12, 0x0a, 0x04, 0x53, 0x65, 0x6e, 0x64, 0x18, 0x06, 0x20, 0x01, 0x28, 0x09, 0x52, 0x04, 0x53,
	0x65, 0x6e, 0x64, 0x62, 0x06, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x33,
}

var (
	file_chat_proto_rawDescOnce sync.Once
	file_chat_proto_rawDescData = file_chat_proto_rawDesc
)

func file_chat_proto_rawDescGZIP() []byte {
	file_chat_proto_rawDescOnce.Do(func() {
		file_chat_proto_rawDescData = protoimpl.X.CompressGZIP(file_chat_proto_rawDescData)
	})
	return file_chat_proto_rawDescData
}

var file_chat_proto_msgTypes = make([]protoimpl.MessageInfo, 1)
var file_chat_proto_goTypes = []interface{}{
	(*Chat)(nil), // 0: pd.Chat
}
var file_chat_proto_depIdxs = []int32{
	0, // [0:0] is the sub-list for method output_type
	0, // [0:0] is the sub-list for method input_type
	0, // [0:0] is the sub-list for extension type_name
	0, // [0:0] is the sub-list for extension extendee
	0, // [0:0] is the sub-list for field type_name
}

func init() { file_chat_proto_init() }
func file_chat_proto_init() {
	if File_chat_proto != nil {
		return
	}
	if !protoimpl.UnsafeEnabled {
		file_chat_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*Chat); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
	}
	type x struct{}
	out := protoimpl.TypeBuilder{
		File: protoimpl.DescBuilder{
			GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
			RawDescriptor: file_chat_proto_rawDesc,
			NumEnums:      0,
			NumMessages:   1,
			NumExtensions: 0,
			NumServices:   0,
		},
		GoTypes:           file_chat_proto_goTypes,
		DependencyIndexes: file_chat_proto_depIdxs,
		MessageInfos:      file_chat_proto_msgTypes,
	}.Build()
	File_chat_proto = out.File
	file_chat_proto_rawDesc = nil
	file_chat_proto_goTypes = nil
	file_chat_proto_depIdxs = nil
}

repository: kinyz/KServer

repo_name: kinyz/KServer


text:
DESCRIPTION = "Music composition program in the style of AMIGA Impulse Tracker"
HOMEPAGE = "http://www.soundtracker.org"
DEPENDS = "gtk+-1.2"
LICENSE = "GPL"
SECTION = "x11/multimedia"
PR = "r0"

SRC_URI = "http://www.soundtracker.org/dl/v0.6/soundtracker-${PV}.tar.gz"

inherit autotools

EXTRA_OECONF = "--disable-asm"

do_configure() {
	oe_runconf
}

SRC_URI[md5sum] = "1e5a2ef689e214933e53f687b3a199bb"
SRC_URI[sha256sum] = "03b2f2c0edba48457ec6178b0a10edfe3481254112936715603a21f638822f50"


text:
// Package printer
// Copyright 2020-2021 Author.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//    http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
//
// Mustafa mbayramo@vmware.com
package printer

import (
	"encoding/json"
	"fmt"
	"github.com/jedib0t/go-pretty/v6/table"
	"github.com/spyroot/tcactl/app/main/cmds/ui"
	"github.com/spyroot/tcactl/lib/client/response"
	"github.com/spyroot/tcactl/pkg/io"
	"github.com/tidwall/pretty"
	"os"
)

//NodesTablePrinter - tabular format printer for node list in node pool
func NodesTablePrinter(nodePool *response.NodePool, style ui.PrinterStyle) {
	t := table.NewWriter()
	t.SetOutputMirror(os.Stdout)
	t.AppendHeader(table.Row{"#", "ID", "Name"})
	for i, pool := range nodePool.Pools {
		for _, node := range pool.Nodes {
			t.AppendRows([]table.Row{
				{i, node.Ip, node.VmName},
			})
		}
		t.AppendSeparator()
	}
	tableStyle, ok := style.GetTableStyle().(table.Style)
	if ok {
		t.SetStyle(tableStyle)
	}
	t.Render()
}

// NodesJsonPrinter - json printer
func NodesJsonPrinter(nodePool *response.NodePool, style ui.PrinterStyle) {

	for _, pool := range nodePool.Pools {
		if style.IsColor() {
			b, err := json.MarshalIndent(pool.Nodes, "", "  ")
			io.CheckErr(err)
			fmt.Println(string(pretty.Color(b, nil)))
		} else {
			err := io.PrettyPrint(pool.Nodes)
			io.CheckErr(err)
		}
	}
}

// NodesYamlPrinter node pool yaml printer
func NodesYamlPrinter(nodePool *response.NodePool, style ui.PrinterStyle) {
	if nodePool == nil {
		return
	}

	DefaultYamlPrinter(nodePool, style)

}

// PoolSpecTablePrinter - tabular format printer for node list in node pool
func PoolSpecTablePrinter(spec *response.NodesSpecs, style ui.PrinterStyle) {
	t := table.NewWriter()
	t.SetOutputMirror(os.Stdout)
	t.AppendHeader(table.Row{"ID", "Lables", "Name", "CPU", "Memory", "Num Nodes", "Status"})
	t.AppendRows([]table.Row{
		{spec.Id, spec.Labels, spec.Name, spec.Cpu, spec.Memory, len(spec.Nodes), spec.Status},
	})

	t.AppendSeparator()
	tableStyle, ok := style.GetTableStyle().(table.Style)
	if ok {
		t.SetStyle(tableStyle)
	}
	t.Render()
}

// PoolSpecJsonPrinter - json printer
func PoolSpecJsonPrinter(spec *response.NodesSpecs, style ui.PrinterStyle) {
	if spec == nil {
		return
	}

	DefaultJsonPrinter(spec, style)
}

// PoolSpecYamlPrinter - json printer
func PoolSpecYamlPrinter(spec *response.NodesSpecs, style ui.PrinterStyle) {
	if spec == nil {
		return
	}

	DefaultYamlPrinter(spec, style)
}

repository: denysaleksandrov/tcactl

repo_name: denysaleksandrov/tcactl