// RadioButtonsTest.cs // Using RadioButtons to set message window options. //using statements go here; // form contains several radio buttons--user chooses one // from each group to create a custom MessageBox private System.Windows.Forms.Label promptLabel; private System.Windows.Forms.Label displayLabel; private System.Windows.Forms.Button displayButton; Label is used to prompt user Label is used to display which button was pressed Display the text Display private System.Windows.Forms.RadioButton questionButton; private System.Windows.Forms.RadioButton informationButton; private System.Windows.Forms.RadioButton exclamationButton; private System.Windows.Forms.RadioButton errorButton; private System.Windows.Forms.RadioButton retryCancelButton; private System.Windows.Forms.RadioButton yesNoButton; private System.Windows.Forms.RadioButton yesNoCancelButton; private System.Windows.Forms.RadioButton okCancelButton; private System.Windows.Forms.RadioButton okButton; private System.Windows.Forms.RadioButton To store user’s choice ofevent One abortRetryIgnoreButton; RadioButtons are created for the enumeration options handling exists for options iconType isall created. Object iconType is a the radio buttons in The enumeration name groupBox1 and groupBox2 private System.Windows.Forms.GroupBox groupBox2; MessageBoxIcon indicate which button private System.Windows.Forms.GroupBox groupBox1; enumeration to display private MessageBoxIcon iconType = MessageBoxIcon.Error; private MessageBoxButtons buttonType = MessageBoxButtons.OK; 16
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Other Rules - Clean Water Rule • Clean Water Rule, 80 Fed. Reg. 37054 (June 29, 2015) • Legal proceedings -Numerous suits filed in numerous district courts and appellate courts -Nationwide stay issued in State of Ohio, et al v. U.S. Corps of Engineers, et al, 803 F3d 804 (6th Cir. 2015) -National Assoc. of Manufacturers v. DOD, US Supreme Court, Jan. 22, 2018 - appellate courts have no jurisdiction • Administrative proceedings -Executive Order #13778 , dated February 28, 2017 - ordered review of Clean Water Rule Seek to define ‘navigable waters’ as per Justice Scalia’s opinion in Rapanos -EPA Intention to Review and Rescind or Revise the Clean Water Rule, 82 Fed. Reg. 12532 (March 6, 2017) Announced two-step process Step 1: Re-establish status quo in place prior to 2015 Rule Step 2: Propose new rule to replace 2015 Rule -Step 1 Reinstated prior rule, 82 Fed. Reg. 34899 (July 27, 2017) Applicability date of 2015 Rule is Feb. 6, 2010, 83 Fed. Reg. 5200 (February 6, 2018) The Applicability Rule was challenged and overturned in 22 states -Step 2 Public meetings, received written recommendations, but no proposed rule at this time EPA announced on October 3 that proposed rule to be issued in 30 days
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03/18/19 18:35 15 Traffic Mining Results 1: TCP,INPUT,129.110.96.117,ANY,*.*.*.*,80,DENY 2: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,80,ACCEPT 3: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,443,DENY 4: TCP,INPUT,129.110.96.117,ANY,*.*.*.*,22,DENY 5: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,22,ACCEPT 6: TCP,OUTPUT,129.110.96.80,ANY,*.*.*.*,22,DENY 7: UDP,OUTPUT,*.*.*.*,ANY,*.*.*.*,53,ACCEPT 8: UDP,INPUT,*.*.*.*,53,*.*.*.*,ANY,ACCEPT 9: UDP,OUTPUT,*.*.*.*,ANY,*.*.*.*,ANY,DENY 10: UDP,INPUT,*.*.*.*,ANY,*.*.*.*,ANY,DENY 11: TCP,INPUT,129.110.96.117,ANY,129.110.96.80,22,DEN Y 12: TCP,INPUT,129.110.96.117,ANY,129.110.96.80,80,DEN Y 13: UDP,INPUT,*.*.*.*,ANY,129.110.96.80,ANY,DENY 14: UDP,OUTPUT,129.110.96.80,ANY,129.110.10.*,ANY,DE NY 15: TCP,INPUT,*.*.*.*,ANY,129.110.96.80,22,ACCEPT 16: TCP,INPUT,*.*.*.*,ANY,129.110.96.80,80,ACCEPT 17: UDP,INPUT,129.110.*.*,53,129.110.96.80,ANY,ACCEPT 18: Rule 1, Rule 2: ==> GENRERALIZATION Rule 1, Rule 16: ==> CORRELATED Rule 2, Rule 12: ==> SHADOWED Rule 4, Rule 5: ==> GENRERALIZATION Rule 4, Rule 15: ==> CORRELATED Rule 5, Rule 11: ==> SHADOWED Anomaly Discovery Result
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13 // UsingMDI.cs // Demonstrating use of MDI parent and child windows. UsingMDI.cs private System.Windows.Forms.MainMenu menuStrip1; private private private private private private System.Windows.Forms.MenuItem System.Windows.Forms.MenuItem System.Windows.Forms.MenuItem System.Windows.Forms.MenuItem System.Windows.Forms.MenuItem System.Windows.Forms.MenuItem File menu fileToolStripMenuItem; newToolStripMenuItem; csToolStripMenuItem; New cppToolStripMenuItem1; submenu pythonToolStripMenuItem; Exit exitToolStripMenuItem; submenu private System.Windows.Forms.MenuItem windowToolStripMenuItem ; Format menu private System.Windows.Forms.MenuItem cascadeToolStripMenuItem; Cascade option private System.Windows.Forms.MenuItem tileHorizontalToolStripMenuItem; private System.Windows.Forms.MenuItem tileVerticalToolStripMenuItem; Tiling options
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5 The Nearest-Neighbor Rule • Let Dn = {x1, x2, …, xn} be a set of n labeled prototypes • Let x’  Dn be the closest prototype to a test point x then the nearestneighbor rule for classifying x is to assign it the label associated with x’ • The nearest-neighbor rule leads to an error rate greater than the minimum possible: the Bayes rate • If the number of prototypes is large (unlimited), the error rate of the nearest-neighbor classifier is never worse than twice the Bayes rate (it can be demonstrated!) • If n  , it is always possible to find x’ sufficiently close so that: P(i | x’)  P(i | x) If P(m | x)  1, then the nearest neighbor selection is almost always the same as the Bayes selection • Pattern Classification, Ch4
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Applying the grammar • Here’s a parse that works for (a*a)+a: E => E+T (rule 1) => T+T (rule 2) => F+T (rule 4) => (E)+T (rule 5) => (T)+T (rule 2) => (T*F)+T (rule 3) => (T*a)+T (rule 6) => (F*a)+F (rule 4 applied twice) => (a*a) + a (rule 6 applied twice)
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An attribute grammar for simple assignment statements  1 Syntax rule: = Semantic rule: .expected_type ← .actual_type 2 Syntax rule: [1] + [2] Semantic rule: .actual_type ← if([1].actual_type=int) and ([2].actual_type = int) then int else real Predicate: .actual_type==.expected_type 3 Syntax rule: Semantic rule: .actual_type ← .actual_type Predicate rule: .actual_type==.expected_type 4 Syntax rule: → A | B | C Semantic rule .actual_type ← look-up(.string)
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Naïve Bayes Filter- Overview “Naïve Bayes classifier is a simple probabilistic classifier based on applying the Bayes theorem with strong (Naïve) independent assumptions.” S e n t e n c e s Pattern Recognizer No Naïve Bayes Unigram No Bigram Yes Parts of Speech No Objective Sentences Yes Multiple Indexer 03/21/19 Page 20
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Conducted 5 experiments Exp 1 Exp 2 Exp 3 Exp 3* Exp 4 Graphs used Attributes used 400 41 400 41 400 4 400 10 400 41 Algorithms 4 4 4 4 4 Useful vs. not-useful yes Useful vs. not-useful Useful vs. not-useful Useful vs. not-useful 9 categories 128 vs. 256 Type of categories 1.Classification Tree 2. SVM 3. Logistic Regression 4. Naive Bayes 1.Classification Tree 2. SVM 3. Logistic Regression 4. Naive Bayes Normalized Order: number of nodes in the component divided by the number of nodes in entire image. Normalized Size: number of edges in the component divided by the number of edges in entire image. Average degree. Density 1.Classification Tree 2. SVM 3. Logistic Regression 4. Naive Bayes Density Average neighbor degree (rnormalized) Pearson coefficient Transitivity Highest Betweenness Maximal matching (divided by number of edges) Maximal matching Number of nodes (percentage from entire image) Degree distribution (best fit) Degree Distribution value 1.Classification Tree 2. SVM 3. Logistic Regression 4. Naive Bayes 10
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