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An open-source project for the Learning Management System used at Coder Academy

Content Past Usage: Attributes 2 through 10 have been used to represent instances. Each instance has one of 2 possible classes: benign or malignant. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In {\it Proceedings of the National Academy of Sciences}, {\it 87}, 9193--9196. -- Size of data set: only 369 instances (at that point in time) -- Collected classification results: 1 trial only -- Two pairs of parallel hyperplanes were found to be consistent with 50% of the data -- Accuracy on remaining 50% of dataset: 93.5% -- Three pairs of parallel hyperplanes were found to be consistent with 67% of data -- Accuracy on remaining 33% of dataset: 95.9% Zhang,~J. (1992). Selecting typical instances in instance-based learning. In {\it Proceedings of the Ninth International Machine Learning Conference} (pp. 470--479). Aberdeen, Scotland: Morgan Kaufmann. -- Size of data set: only 369 instances (at that point in time) -- Applied 4 instance-based learning algorithms -- Collected classification results averaged over 10 trials -- Best accuracy result: -- 1-nearest neighbor: 93.7% -- trained on 200 instances, tested on the other 169 -- Also of interest: -- Using only typical instances: 92.2% (storing only 23.1 instances) -- trained on 200 instances, tested on the other 169 Relevant Information: Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself: Group 1: 367 instances (January 1989) Group 2: 70 instances (October 1989) Group 3: 31 instances (February 1990) Group 4: 17 instances (April 1990) Group 5: 48 instances (August 1990) Group 6: 49 instances (Updated January 1991) Group 7: 31 instances (June 1991) Group 8: 86 instances (November 1991) Total: 699 points (as of the donated datbase on 15 July 1992) Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. This is because it originally contained 369 instances; 2 were removed. The following statements summarizes changes to the original Group 1's set of data: Group 1 : 367 points: 200B 167M (January 1989) Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805 Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial : Changed 0 to 1 in field 6 of sample 1219406 : Changed 0 to 1 in field 8 of following sample: : 1182404,2,3,1,1,1,2,0,1,1,1 Number of Instances: 699 (as of 15 July 1992) Number of Attributes: 10 plus the class attribute Attribute Information: (class attribute has been moved to last column) Attribute Domain Sample code number id number Clump Thickness 1 - 10 Uniformity of Cell Size 1 - 10 Uniformity of Cell Shape 1 - 10 Marginal Adhesion 1 - 10 Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Class: (2 for benign, 4 for malignant) Missing attribute values: 16 There are 16 instances in Groups 1 to 6 that contain a single missing (i.e., unavailable) attribute value, now denoted by "?". Class distribution: Benign: 458 (65.5%) Malignant: 241 (34.5%) Acknowledgements O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers). Inspiration Rouse Tek Bio informatics Cytogenomics Project is an attempt to bring the human genome to the understanding of how cancers develop. All of our bodies are composed of cells. The human body has about 100 trillion cells within it. And usually those cells behave in a certain fashion. They observe certain rules, they divide when they’re told to divide, they’re quiescent when they’re told to remain dormant, they stay within a particular position within their tissue and they don’t move out of that. Occassionally however, a single cell, of those 100 trillion cells, behave in a different way. That cell keeps dividing when all its signals around it tell it to stop dividing. That cell ignores its counterparts around it and pushes them out of the way. That cell stops observing the rules of the tissue within which it is located and begins to move out of its normal position, invading into the tissues around it and sometimes entering the bloodstream and becoming a metastasis, depositing in another tissue of the body.. The reason the cell has gone rogue is because it has acquired within its genome, within its DNA, a number of abnormalities that cause it to behave as a cancer cell. All 100 trillion cells in the human body have got a copy of the human genome, they have 2 copies, 1 maternal, 1 paternal. Throughout Life all those copies of the genome in those 100 trillion cells, are acquiring abnormal changes or somatic mutations. These mutations are present in the cell and are not transmitted from parents to offspring. They are constrained to that individual cell. Those mutations occur in every cell of the body, normal and abnormal, for a number of different reasons. They occur because every time a cell divides possibly one letter of code out of 3 billion is replicated incorrectly. And that’s 1 source of somatic mutations. Another source is that our 100 trillion cells are being exposed to a number of different onslaughts like radiation, self generated chemicals from inhalation of things like tobacco smoke or even an unhealthy diet over time. Occasionally mechanisms in a particular cell make breakdown and the DNA of that cell begins to acquire somatic mutations rather more commonly than other cells. So in summary, every cell in the body acquires mutations throughout a lifetime, and as we get older we acquire more and more somatic mutations in which occasionally a particular type of gene is mutated where the protein that it makes is abnormal and drives the cell to behave in a rogue fashion that we call cancer.

Source code repository for SEDC Code Academy @Skopje 8.0 4th subject 'Advanced JS Development' students exercises.

Source code repository for SEDC Code Academy 7.0 5th subject 'OOP with C#' students exercises.

Source code for Jedi Academy by Raven Software.

Source code for the Introduction to Programming using Java from DevDojo Academy

齐鲁工业大学(山东省科学院) 自然语言处理与认知计算研究团队官方网站 （源代码仓库） Source code tree of official website of Research Group for Natural Language Processing and Cognitive Computing of Qilu University of Technology (Shandong Academy of Sciences)

Source code repository for SEDC Code Academy 7.0 4th subject 'Advanced JS Development' students exercises.

This document describes the team work assignment for Telerik Academy students studying Object-Oriented Programming (OOP) – February 2014. Project Description Design and implement an object-oriented Role Playing Game by choice. Here are some suggestions: - The world can be fantasy, sci-fi, modern, etc. - You may have one or more heroes, gaining experience, having skills, wearing items, etc. - You may have one or more players, fighting against a computer or against each other - You may have enemies like creatures, machines, warriors, etc. - You may have items like swords, guns, armor, space-suits, etc. - You may have skills like double-damage, teleportation, etc. General Requirements Please define and implement the following object-oriented assets in your project: • At least 5 interfaces (with one or more implementations) • At least 15 classes (implementing the application logic) • At least 3 abstract class (with inheritors) • At least 1 exception class (with usage in your code) • At least 3 levels of depth in inheritance • At least 1 polymorphism usage • At least 1 structure • At least 1 enumeration • At least 1 event (with subscribers) • At least 1 design pattern (e.g. Composite, Singleton, Factory, Wrapper, Bridge, Command, Iterator, …) You might read about design patterns in Wikipedia, Sourcemaking, DoFactory and others. Additional Requirements • Follow the best practices for OO design: use data encapsulation, use exception handling properly, use delegates and events like it is recommended in MSDN, use inheritance, abstraction and polymorphism properly, follow the principles of strong cohesion and loose coupling. • Obligatory use Git to keep your source code and for team collaboration (you might use https://github.com/). TFS or SVN are not allowed. Use Git. • Provide a class diagram (to visualize all types). Optional Requirements If you have a chance, time and a suitable situation, you might add some of the following to your project: • Static members (fields, properties, constructor, etc.) • Constants, generic types, indexers, operators • Lambda expressions and LINQ • Implementation of IEnumerable<T>, ICloneable, ToString() override • Namespaces (if your classes are too much) • User interface (UI) – console, graphical, web or mobile Non-Required Work • Completely finished project is not obligatory required. It will not be a big problem if your project is not completely finished or is not working greatly. This team work project is for educational purpose. Its main purpose it to experience object-oriented modeling and OOP in a real-world project and to get some experience in team working and team collaboration with Git. Deliverables Put the following in a ZIP archive and submit it (each team member submits the same file): • The complete source code. • Brief documentation of your project (2-3 pages). It should provide the following information (in brief): o Team name and list of team members o Project purpose – what problem do you solve? o Class diagram of your types o The URL of your Git repository o Any other information (optionally) • Optionally provide a PowerPoint presentation designed for the project defense. Public Project Defense Each team will have to deliver a public defense of its work in from of the other students and trainers. You will have only 10 minutes for the following: • Demonstrate the application (very shortly). • Show the class diagram (just a glance). • Show the source code in the Git web-based source code browser. • Show the commits logs to confirm those team members who have contributed. • Optionally you might prepare a PowerPoint presentation (3-4 slides). Please be strict in timing! Be well prepared for presenting maximum of your work for minimum time. Bring your own laptop. Test it preliminary with the multimedia projector. Open the project assets beforehand to save time. You have 10 minutes, no more. Give Feedback about Your Teammates You will be invited to provide feedback about all your teammates, their attitude to this project, their technical skills, their team working skills, their contribution to the project, etc. The feedback is important part of the project evaluation so take it seriously and be honest.

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Source code repository for SEDC Code Academy @Skopje 8.0 3rd subject 'Basic JS Development' students exercises.

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