UI Issues, Neural Nets, RTS

UI Issues, Neural Nets, RTS CSE 490RA January 27, 2005

Lecture outline • Leftovers from UI Lecture • Neural Networks • RTS

Uses of the stylus • Direct writing • Abstract writing • Pointing • Selecting • Gesture • Direct manipulation • Control

Mode issues in pen computing • Adding modes to the pen • Barrel button • Secondary button with non-dominant hand • Eraser tip • Pressure • Explicit mode buttons • Area based modes (writing area, gesture area, control area, etc.) • Cursor feedback • Importance of visual cues for informing user • Errors in crossing mode boundaries

Control • Widgets design for stylus use

Flow Menu • Use movement through octants for control information Move Item 100% 100% Text Item Highlight Zoom 66.6% 200% 66.6% 200% Shape 50% 75.0 400% 50% 100.0 400% 25% 800% 25% 800% Custom Custom

Interaction with direct manipulation Move Item Highlight Zoom

CrossY: Crossing based UI • Specify operations by drawing through

Gestures • Commands issued with a single stroke • May be drawn or invisible • Support from SDK • Register gestures to be recognized • UI Issues • Similar to keyboard short cuts • Speed up for experts • Hard to learn / remember

Gestures • Ambiguity • Distinction between gestures • Distinction between gesture and other ink • Robustness • Handling misrecognized gestures • False positive • False negative • Gesture initiated actions should be undoable

Neural Networks • Fundamentals for Handwriting Reco Lecture (Jay Pittman) • Recognition algorithm • Learning based recognition algorithm

General considerations for learning algorithms • Training sets • Collection • Evaluation • Training cost • Time and space • Algorithm cost • Time and space • Robustness to error

Neural networksPerceptrons • Motivated by considerations of the brain

Single layer neural networks • Bias weights • Threshold activation function • Step function • Sigmoid function: 1/(1 + e-x)

What you can do with single layer networks • Any linearly separable dataset can be recognized with a single layer neural network

Gradient descent algorithm • Choose initial weights • While not at optimum • Compute derivative • Move along derivative • It can be proved this converges

However, single layer networks are very limited

Multilayer networks with hidden nodes • Can recognized much wider range of data set • The gradient descent algorithm generalizes to this case

Real Time Stylus • Allow for user computation on the ink thread

Architecture (Overview) RealTime Event Sink RealTime Event Sink RealTime Event Sink RealTimeStylus Ink Collecting Object queue storage Pen Service InkCollector “Inking” Thread UI Thread

Substroke operations • Examples • Custom Inking • Multiple Ink • Distributed Ink • Rendering • Dynamic – draw on ink packet • Static – draw on paint event

Custom Inking • Create plugin to listen for packets • Registor for Packets • Draw triangle on each packet

public void Packets(RealTimeStylus sender, PacketsData data){ for (int i = 0; i < data.Count; i += data.PacketPropertyCount){ Point point = new Point(data[i], data[i+1]); // Packet data always has x, y // followed by the rest Point convertedPoint = new Point(); // We need to convert to Pixels... convertedPoint.X = (int)Math.Round((float)point.X * (float)myGraphics.DpiX/2540.0F); convertedPoint.Y = (int)Math.Round((float)point.Y * (float)myGraphics.DpiY/2540.0F); if (this.pointCount == 0) this.firstPoint = convertedPoint; if (this.pointCount > 1){ Color color = pointCount % 2 == 0 ? Color.Red : Color.Yellow; Point[] triangle = new Point[3]; triangle[0] = this.firstPoint; triangle[1] = this.previousPoint; triangle[2] = convertedPoint; myGraphics.FillPolygon(new SolidBrush(color), triangle); } this.pointCount++; this.previousPoint = convertedPoint; } }

Distributed Ink • Capture ink packets on machine 1 • Send packets to machine 2 • Reconstruct ink stroke by inserting packets

Sample application

Distributed Ink • Sender: • Collect packets in packet, when count is above threshold, send message • Receiver: • Insert packets as custom data • Render custom data as it is received • Assemble custom data as an ink stroke

UI Issues, Neural Nets, RTS