Sai Yagnyamurthy, Director of Corporate Strategy at Ford Motor Company, to join DAV Foundation Advisory Board
The DAV Foundation is pleased to announce Sai Yagnyamurthy, the Director of Corporate Strategy at Ford Motor Company, as the latest addition to the foundation’s advisory board.


Reelsounds Chicago opened in 1995 with the sole mission to create the most comfortable quarters for musicians and engineers to make music. The studio is centered on the equipment that was used to make the classic recordings. Digital friendly, but with an analog heart, Reelsounds is the sultry collision of present and past.
The spacious yet cozy interior is modeled on the feel of America's classic recording studios, and affords clients the opportunity to capitalize on the energy and inspiration that only comes from locking in and playing as a unit. While the facility specializes in capturing basic tracks for bands, it also accommodates solo recordings, overdubs, and mixing with a "big room" touch.


Co//ab has many facilities that are available for students and entrepreneurs to reserve. Co//ab has 4 types of rooms: the large classroom space, a social space at the front, and a small "clinic" space that can be used for sessions with one of Co//ab's many mentors.
Amenities of Co//ab include coffee available around the clock, the first North State fiber Internet business customer in downtown Greensboro, and mentoring from our professionals. Currently, we have three lawyers on our staff who are available to counsel early stage entrepreneurs in the steps necessary to get their new company off the ground.
Large Classroom Workspace
"Clinic" Space
"Desks


Binance CEO Says Bitcoin Will Hit $16,000 “Soonish”
by Christine VasilevaBinance CEO, Changpeng Zhao, has moved boldly with another prediction. This time, seeing Bitcoin hit $16,000 soon.
CZ Among Top Bitcoin Influencers
Binance’s CEO has been one of the most influential voices in crypto, though not without critics and detractors. But CZ remains bullish on his Twitter channel, recently making a prediction for BTC to drive to a high not seen in more than 18 months.
Who were the 1.4 billion people working on Bitcoin’s boost was unclear – the global crypto community looks much smaller. It is possible Binance’s CEO envisioned much of China joining in the game with renewed enthusiasm.
Binance has been bullish not only on BTC but also on China’s relationship with cryptocurrencies as well. Recently, the exchange opened an OTC desk for Chinese traders, and Zhao mentioned Alipay and WeChat as tools to buy BTC with.
Additionally, Binance is opening a new office in mainland China, to grab onto the increased demand for crypto-related services.
In the past, the predictions of CZ also had bad moments, especially when BTC flash-crashed down from the $10,000 level. Shortly before that, CZ was confident traders would regret selling below $10,000, and possibly never see four-digit BTC again.
Justin Sun Bashed for Making “Borderline Price Prediction”
bitcoin binance CZ
But the more recent exposure came from Justin Sun, another social media star in the world of crypto. Sun posted a now-deleted tweet suggesting TRON (TRX) may be a top 4 coin by the end of the year. The reply of @cz_binance called this a “borderline price prediction” and told Sun “don’t do it”.
Twitter commentators were quick to pick up on this, and gave CZ a taste of his own medicine.
Both Zhao and Sun have a view of the Chinese market for investment products and technology. But while Sun never misses a beat in touting TRON, Zhao is more of a Bitcoin proponent. No wonder, since Binance is now fighting for a top position in trading BTC futures, one of the largest, most liquid and most lucrative markets.
For now, BTC fails to rally based only on Zhao’s expectations. A year-end rally is one possible deadline, and BTC is certainly seen breaking out before the halving of the reward. But predicting BTC is risky due to its propensity to shift directions suddenly. The Bitcoin fear and greed index is currently stuck in the middle on neutral at 50 points, not giving a sign for either direction.
What do you think about CZ’s bold Bitcoin prediction? Share your thoughts in the comments section below!
Images via Shutterstock, Twitter @cz_binance @Justinsuntron @proofofpork @stylist_crypto
The Rundown


- CalculateNewLocation() - Method in class ModelLayer.Car
-
Calculates the new location for the car based on
its current position, current speed and direction (angle)
- CalculateNextFrame() - Method in class ModelLayer.Car
-
Called with every new frame.
- Car - Class in ModelLayer
-
Manages a car's movement and calculates coordinates.
- Car(int) - Constructor for class ModelLayer.Car
-
Manages a car's movement and calculates coordinates.
- CAR_Crashed_ImageFileNameWithPath - Static variable in class ControlLayer.SharedResources
-
- CAR_Image_Size_X - Static variable in class ControlLayer.SharedResources
-
- CAR_Image_Size_Y - Static variable in class ControlLayer.SharedResources
-
- CAR_ImageFile_Angles - Static variable in class ControlLayer.SharedResources
-
- CAR_ImageFile_RootPath - Static variable in class ControlLayer.SharedResources
-
- CAR_ImageFileName_Extension - Static variable in class ControlLayer.SharedResources
-
- CAR_ImageFileName_Prefix - Static variable in class ControlLayer.SharedResources
-
- CAR_Simulated_Angle_Values - Static variable in class ControlLayer.SharedResources
-
- CAR_Simulated_Fine_Bounds - Static variable in class ControlLayer.SharedResources
-
- CAR_Starting_Angle - Variable in class ModelLayer.MapML.MapModel
-
Represents the cars starting angle, whereby 0 is a car facing north.
- CAR_StartingPoint_X_Player1 - Variable in class ModelLayer.MapML.MapModel
-
The X coordinate of the starting point for the player 1 car.
- CAR_StartingPoint_X_Player2 - Variable in class ModelLayer.MapML.MapModel
-
The X coordinate of the starting point for the player 2 car.
- CAR_StartingPoint_Y_Player1 - Variable in class ModelLayer.MapML.MapModel
-
The Y coordinate of the starting point for the player 1 car.
- CAR_StartingPoint_Y_Player2 - Variable in class ModelLayer.MapML.MapModel
-
The Y coordinate of the starting point for the player 2 car.
- CarAccelerationOccurred() - Method in class ControlLayer.GameEngine
-
Called when a car accelerates.
- CarAccelerationStopped() - Method in class ControlLayer.GameEngine
-
Called when a car stopped accelerating.
- CarBounds - Class in ModelLayer.CollisionManagement
-
Represents a car's edges for a given angle.
- CarBounds(int, int, int, int, int, int, int, int) - Constructor for class ModelLayer.CollisionManagement.CarBounds
-
Represents a car's edges for a given angle.
- CarCrashOccurred() - Method in class ControlLayer.GameEngine
-
Called when two cars collide with each other.
- CarImpactOccurred() - Method in class ControlLayer.GameEngine
-
Called when a car hits an another non-car object on the map (wall, tree etc).
- CarInGameDisplayLabel - Class in ViewLayer.Screens.InGameScr
-
Builds on a JLabel to display images of a car (in different angles) on the screen.
- CarInGameDisplayLabel(Player) - Constructor for class ViewLayer.Screens.InGameScr.CarInGameDisplayLabel
-
Builds on a JLabel to display images of a car (in different angles) on the screen.
- CarSelectionPanel - Class in ViewLayer.Screens.LaunchScr
-
Uses a JPanel to display user selectable car images.
- CarSelectionPanel(int) - Constructor for class ViewLayer.Screens.LaunchScr.CarSelectionPanel
-
Uses a JPanel to display user selectable car images.
- ChangeGameSessionCar(int, int) - Method in class ControlLayer.Controller
-
Changes the selected car for a player in a game session.
- ChangeGameSessionMap(int) - Method in class ControlLayer.Controller
-
Changes the selected map.
- CheckForCollisionAndMove(Point) - Method in class ModelLayer.Car
-
Checks for collisions based on the provided new location as a parameter.
- CLI_Background_Color - Static variable in class ControlLayer.SharedResources
-
- CLI_Background_Image_Height - Static variable in class ControlLayer.SharedResources
-
- CLI_Background_Image_Width - Static variable in class ControlLayer.SharedResources
-
- CLI_Header_Font - Static variable in class ControlLayer.SharedResources
-
- CLI_Height - Static variable in class ControlLayer.SharedResources
-
- CLI_HorizontalSpacing_From_CSP - Static variable in class ControlLayer.SharedResources
-
- CLI_ImageFile_FileExtension - Static variable in class ControlLayer.SharedResources
-
- CLI_ImageFileName_NoSubFix - Static variable in class ControlLayer.SharedResources
-
- CLI_LayoutImage_FilePath - Static variable in class ControlLayer.SharedResources
-
- CLI_Width - Static variable in class ControlLayer.SharedResources
-
- CollisionManager - Class in ModelLayer.CollisionManagement
-
Calculates collisions between map objects and cars.
- CollisionManager(ArrayList<Player>, ArrayList<MapObject>) - Constructor for class ModelLayer.CollisionManagement.CollisionManager
-
Calculates collisions between map objects and cars.
- ControlKeyPressed(int) - Method in class ViewLayer.Screens.InGameScr.CarInGameDisplayLabel
-
Called when one of the control keys are pressed.
- ControlKeyReleased(int) - Method in class ViewLayer.Screens.InGameScr.CarInGameDisplayLabel
-
Called when one of the control keys are released from pressing.
- ControlLayer - package ControlLayer
-
- Controller - Class in ControlLayer
-
Switches between the main flows of the application.
- Controller() - Constructor for class ControlLayer.Controller
-
- CreateCarLabels() - Method in class ViewLayer.Screens.InGameScr.InGameScreen
-
- CreateDefaultWindow() - Method in class ViewLayer.SwingUICore
-
Display the Frame
- CreateHeadsUpDisplays() - Method in class ViewLayer.Screens.InGameScr.InGameScreen
-
- CreateHudComponents() - Method in class ViewLayer.Screens.InGameScr.HeadsUpDisplayPanel
-
Instantiates the HUD elements.
- CreateMapObjects() - Method in class ModelLayer.MapML.EasyMapML
-
Creates the MapObjects which are used for collision detection.
- CreateMapObjects() - Method in class ModelLayer.MapML.MapModel
-
Instantiates the MapObjects and adds them to the _MapObjects array list.
- CreateMapObjects() - Method in class ModelLayer.MapML.MediumMapML
-
- CreateOutOfMapMapObjects(ArrayList<Line2D>) - Method in class ModelLayer.MapML.MediumMapML
-
- CreatePlayerHeaderLabel(int) - Method in class ViewLayer.Screens.LaunchScr.CarSelectionPanel
-
Adds a JLabel header containing text with the player's number and an istruction to select car (e.g.
- CreatePlayerIconLabel() - Method in class ViewLayer.Screens.InGameScr.HeadsUpDisplayPanel
-
Instantiates the JLabel which displays the icon/thumbnail of the palyer's car.
- CreatePlayerNameLabel() - Method in class ViewLayer.Screens.InGameScr.HeadsUpDisplayPanel
-
Instantiates the JLabel that contains the player's name.
- CreateSpeedDisplayLabel() - Method in class ViewLayer.Screens.InGameScr.HeadsUpDisplayPanel
-
Instantiates the JLabel which displays the speed as a text.
- CreateSpeedProgressBar() - Method in class ViewLayer.Screens.InGameScr.HeadsUpDisplayPanel
-
Instantiates the progress bar representing the current speed.
- CSP_Error_Selectable_Car_Image_Missing - Static variable in class ControlLayer.SharedResources
-
- CSP_Header_Color - Static variable in class ControlLayer.SharedResources
-
- CSP_Header_Font - Static variable in class ControlLayer.SharedResources
-
- CSP_Header_Height - Static variable in class ControlLayer.SharedResources
-
- CSP_Header_Message_After_Player_Number - Static variable in class ControlLayer.SharedResources
-
- CSP_Location_Player1_X - Static variable in class ControlLayer.SharedResources
-
- CSP_Location_Player1_Y - Static variable in class ControlLayer.SharedResources
-
- CSP_Location_Player2_X - Static variable in class ControlLayer.SharedResources
-
- CSP_Location_Player2_Y - Static variable in class ControlLayer.SharedResources
-
- CSP_Number_Of_Selectable_Cars - Static variable in class ControlLayer.SharedResources
-
- CSP_Selectable_Car_Holder_Color - Static variable in class ControlLayer.SharedResources
-
- CSP_Selectable_Car_Holder_Spacing - Static variable in class ControlLayer.SharedResources
-
- CSP_Selectable_Car_Image_FileName_NoPrefix - Static variable in class ControlLayer.SharedResources
-
- CSP_Selectable_Car_Image_FileNamePath - Static variable in class ControlLayer.SharedResources
-
- CSP_Selectable_Car_Image_Size - Static variable in class ControlLayer.SharedResources
-
- CurrentGameSession - Class in ControlLayer
-
Represents one game session, containing references to all the participant objects and related current game settings.
- CurrentGameSession() - Constructor for class ControlLayer.CurrentGameSession
-


This document is intended to serve as a resource for other developers to get a familiarity for how the different parts of the Affordable Medical Ultrasound Training Simulator function together. It is not a look at every nook-and-cranny in the entire codebase, but an informal top-down walkthrough to guide you through the data flow and structure of the project. It should be accompanied by class documentation at ./Docs/html/index.html - there are links to docs pages that may be broken if the class documentation isn't located there.
I am assuming that you already have some experience with Unity3D - at least enough to understand terms like "Inspector", "MonoBehaviour", "Component", and "Raycast". I am also assuming that you have a bit of background knowledge about medical ultrasound procedures and physics - at least enough to understand terms like "B-mode" and "acoustic impedance".
Logan M Hood
2014-05-01
The "Affordable Medical Ultrasound Training Simulator" project (henceforth referred to as just "the simulator") is in fact two separate modules:
A good entry point to understanding the Simulator is the "Basic Geometry" scene. This is its path in the project folder:
Assets/Scenes/TestScenes/horayValidationTests/basicGeometry.unity
Open this scene in the Unity editor.
There are five objects in the scene:
When you run the scene, you'll see a black and white image rendered to the display. This image is actually a simulated ultrasound image - it's just a cross-section of the organs in the scene, from the perspective of the blue cube. Initially, you're seeing a slice going through the "ground" plane (y = 0). Try moving the cube around in the Scene view while the scene is running, and you'll see the image change accordingly.
The first object we'll look at is the Display. The Display has two responsibilities: to continuously display a 2D texture containing the ultrasound image (without worrying about how it's rendered), and to initially set up all the classes necessary to generate the image. Find the Display object in the scene (it's a child object of the Main Camera). Look at the DisplayBehavior component in the inspector: there are 3 inspector variables. Texture Width and Height just change the dimensions of the texture on the display. You shouldn't need to change these very much, except to adjust for performance reasons.
The Display Mode variable is very important - it actually determines the configuration of the entire rendering pipeline of the ultrasound image. It's set to HORAY by default. Recall that HORAY is a name for the ultrasound simulation model we are using. Try changing the Display Mode to InvHORAY and running the scene. You should see exactly the same image on the Display, but the colors have been inverted.
Now try changing the Display Mode to FakeTexture. This sets up the Display to use a test class that provides a constant texture. You'll see that the display now shows vertical color bars, nothing resembling an ultrasound image. This is the simplest configuration possible - let's look at how it works.
Open up the DisplayBehavior script. There are two private variables in this class:
private Texture2D texture;
private ITextureSource textureSource;
Texture2D is a built-in class in the Unity Engine, and allows us to programmatically change the texture of an object (rather than the more common use of static textures in games). ITextureSource is an interface describing a class that renders some image to a Texture2D - maybe an ultrasound image, or maybe horizontal color bars. We'll look more closely at it later, but for now, it's enough to just know that the textureSource variable is the class that's actually providing the texture for the display.
Next, look at the Start( ) method in DisplayBehavior. It starts with a log statement, then the texture is instantiated and assigned to renderer.material.mainTexture - this just tells Unity3D that we'll be using a dynamic texture for the display. Next is a long switch statement, switching on the Display Mode value that we just changed in the Inspector. Recall that we set the mode to FakeTexture earlier. In the case for FakeTexture, we see the textureSource is set by the method DisplayTexturePipelineFactory.BuildFakeTextureSource()
.
"DisplayTexturePipelineFactory" sounds a bit like something out of FizzBuzzEnterpriseEdition, but it's not anything too terrible. It's just a factory class, and it's in the same file as DisplayBehavior if you scroll down a bit further. There is one method in the DisplayTexturePipelineFactory for each possible Display Mode. Several of the factory methods are a bit more complicated, but the method we're using, BuildFakeTextureSource(), just returns a new instance of TestTextureSource.
Now that we know that TestTextureSource is being used as the TextureSource for the Display, let's look at how the Display makes use of it. Scroll back up to the Update( ) method in DisplayBehavior. Remember that since DisplayBehavior extends MonoBehaviour, the Update( ) method is automatically called once every frame. Apart from some logging, the only action actually performed in the Update method is just a call to render the texture:
textureSource.RenderNextFrameToTexture(ref texture);
Now let's take a closer look at how TestTextureSource works. Actually, it's worth looking first at the interface for this class, ITextureSource. Only a single method is specified in this interface:
void RenderNextFrameToTexture(ref Texture2D texture);
TestTextureSource only has to implement this rendering callback method. Take a look at its implementation of RenderNextFrameToTexture - there are three important steps:
Color[] pixels = new Color[width * height];
texture.SetPixels(pixels);
texture.Apply();
Return to the inspector, and change the Display object's DisplayMode to FakeProbeOutput. When you hit play, you'll see something superficially resembling an ultrasound image, but, as the name suggests, this is also a fake image. However, with this mode enabled, we'll be able to step deeper through the display code than before.
Remember that the DisplayMode variable is used in the Start( ) method of DisplayBehavior. This time, we call the method BuildWithFakeBModeProbeOutput to initialize our TextureSource. Here's the body of this factory method:
IProbeOutput fakeProbeOutput = new TestProbeOutput();
IImageSource imageSource = new BModeOutputImageDecoder(fakeProbeOutput);
return new TextureSource(imageSource);
We can imagine that our factory method is plugging these classes into one another, like so:
TestProbeOutput -> BModeOutputImageDecoder -> TextureSource -> Display
As before, let's explore the code from the top down. We already know that the Display is receiving a Texture2D from the TextureSource class. Let's investigate the TextureSource class to see how it implements the RenderNextFrameToTexture callback. It follows the same pattern as our TestTextureSource did earlier:
imageSource.RenderColorImageInBitmap(ref colorBitmap);
(As an aside, a ColorBitmap is just a struct containing a Color array along with the dimensions (height and width) of the bitmap.)
Let's take a closer look at the BModeOutputImageDecoder now. Specifically, look at the RenderColorImageInBitmap method. There are three key steps in this method:
For all intents and purposes, an UltrasoundScanData object is structurally just a jagged array. The "outer" array is a list of UltrasoundScanline objects. Each UltrasoundScanline is a discrete trajectory through space, radially omitted from the ultrasound probe. Then, points along each UltrasoundScanline are sampled to create "inner" arrays of discrete UltrasoundPoints. For now, think of an UltrasoundPoint as a tuple <X,Y,Z,B>, where X Y Z are the coordinates in world space of that UltrasoundPoint, and B is the "brightness" of that point, as it should appear when rendered in the ultrasound image.
Here is an example of a hypothetical UltrasoundData object called "data":
data[0] = scanline along vector (1,2,3) = <0,0,0,1> -> <1,2,3,1> -> <2,4,6,0> -> <3,6,9,1>
data[1] = scanline along vector (0,0,1) = <0,0,0,1> -> <0,0,1,1> -> <0,0,2,0> -> <0,0,3,0>
data[2] = scanline along vector (0,1,0) = <0,0,0,1> -> <0,1,0,1> -> <0,2,0,1> -> <0,3,0,1>
Let's return to our BModeOutputImageDecoder and see how this data is rendered. Since the UltrasoundScanData is a jagged array, it makes sense to iterate through the data with a nested for-loop. The basic pseudo-code of this iteration process is:
foreach (UltrasoundScanline scanline in data)
foreach (UltrasoundPoint point in scanline)
draw(point);
The draw code is actually a bit more complicated than that - first we have to figure out which pixel coordinate that point should be drawn to (computed by the MapScanningPlaneToPixelCoordinate method), but those details aren't critical to understand. All that is really necessary to know is that the BModeOutputImageDecoder can generate a bitmap representation of the data in an UltrasoundScanData.
Remember how in the Overview section I said that we can think of the "host" project as separate from the actual ultrasound simulation? At this point, you've seen all the basics of the host project pertaining to image display. Next, we'll look at the ultrasound simulation portion - specifically, how the HORAY system can be used to populate an UltrasoundScanData object.


Cloud Gaming for THREE.js
What are we gonna talk about ?
- Possible solution for cloud gaming
- apply to any webgl games
- cheap idea for cloud gaming for webgl
status : experimental, naive but working surprisingly well!
What is Cloud Gaming ?
- wikipedia definition
- It is new. no widely accepted definition
- usually implies render on server feature
Rougly…
Trying to run a game written for device A
on a device B, typically dumber than A.
What is Our Use Case
Let's clear the dust a bit...
- we run a webgl game in browser
- player device is a non-webgl mobile phone
Goal : to make that game playable on your player device
Our Goal
- To display a webgl game
- on a non-webgl player device
Simple Enougth...
Early data - rendering
- So we got to render our game in server
- it will be rendered via a web browser
- seems natural choise to render webgl
Early data - architechture
3 parts
- a renderer : the brower running our game
- a controller : used by players to access our game
- a server : ensures the communication between both
Early data - networking
- networking done thru websocket
- no webrtc at the moment
- websocket is available a lot more than webrtc
- especially on mobile phone, our usecase.
Early data - Software
- server in node.js
- websocket are trivial with socket.io
- little js library in renderer, and another in controller
- only common technology here
implementation is easy
Principles Of Operations
- on server side you got a browser web running the game
- On this server you take regular screenshot .toDataUrl
- You send them to the player device... mobile phone, tablette whatever.
Principles Of Operations
- Thus the player can see webgl rendering on any device.
- The player use touch screen as inputs
- They are then sent to the server which send them back to the browser.
Principles Of Operations
- So we got the whole loop. The browser receives inputs from the player.
- And the player sees what is displayed in the browser.
Principles Of Operations
- this is actual cloud gaming! All that with a simple node.js server to write and some js code. Maybe a few days. Surely less than a week.
- Performances are left as an exercice to the reader :)
3 areas affect network performances
- how long if the network path
- how much data you send thru this path
- what is the latency of this path
Possible Improvements
send data directly from the renderer to the controller
- avoid one hop in the network path
- may improve latency
Possible Improvements
Using WebRTC ?
- webrtc allows p2p communication on spec.
- is it on the remote controller of your usecase?
- Quite young feature tho
- its availability and reliability should be tested
- webrtc allows to screen share
- maybe it could stream our rendering efficiently
- with a nice compressed video format
Possible Improvements
send smaller data
- send image thru a more compressed format
- tried png/jpg/webp
- webp is smaller
- send image thru a movie format
Could that be the same game ?
difference of inputs
- desktop inputs
- large screen, keyboard and mouse
- mobile phone inputs
- small screen and touch screen
- no keyboard, no mouse
Could that be the same game ?
Difference of network capabilities
- mobile network slower than desktop network
- mobile controller requires more from network
- to send player inputs to game renderer
- to receive rendering display from server
Could that be the same game ?
Difference of network latency
- on desktop, no noticable latency
- on mobile, it has more latency by design
Could that be the same game ?
Nope
- big network requirement outside our controls
- mobile network capabilities
=> games needs to be adapated to controller capabilities
Where to get threex.cloudgaming.js


UK advertisers block ‘Trump’ from campaigns amid fears over brand reputation
by James WarringtonThe vast majority of UK advertisers have blocked their campaigns from appearing next to references to Donald Trump amid fears the US President could damage their brand.
On average, 87 per cent of campaigns using keyword blocking between March and September excluded the word ‘Trump’, according to new data from Integral Ad Science.
Read more: Twitter pulls all political advertising as voters support regulation
Nigel Farage was the most excluded British politician, with 59 per cent of campaigns placing the Brexit Party leader on their blacklists.
Keyword lists allow advertisers to specify any terms that they do not want their adverts to appear alongside online, ensuring their brand image or reputation is not damaged.
However, the research revealed that UK brands were more concerned about the risk from the US political agenda than from troubles closer to home.
Prime Ministers Theresa May and Boris Johnson were excluded on just six per cent and five per cent of campaigns respectively, while Labour leader Jeremy Corbyn was blocked in less than one per cent of cases.
Advertising’s most-blocked political figures
| Political figure name | Exclusion rate (%) |
| Donald Trump | 87 |
| Nigel Farage | 59 |
| Tommy Robinson | 17 |
| Theresa May | 6 |
| Boris Johnson | 5 |
| Jeremy Corbyn | 0 |
Moreover, only 21 per cent of brands opted to blacklist the more general term ‘Brexit’, despite a small peak following May’s resignation in late May.
Exclusion of the term ‘UKIP’ also fluctuated over the last three months, with only one per cent of campaigns blocking references to the party near the first deadline to leave the EU, compared to eight per cent over the six-month period.
Some brands, however, chose to avoid the political minefield entirely, and the general term ‘politics’ was banned by almost 60 per cent of campaigns. ‘Westminster’ was the most-blocked political term, peaking at roughly 90 per cent in July.
“Risk thresholds are unique to each brand, however it is interesting to see in the current political climate that many brands are choosing to exclude political keywords – and even more fascinating that UK brands see content aligned to ‘Trump’ as having a greater impact on their brand’s reputation, than their own country’s political turmoil,” said Nick Morley, EMEA managing director at Integral Ad Science.
Brand safety has become an increasingly important consideration for advertisers amid concerns campaigns have been appearing alongside harmful or inappropriate content.
In June top brands including Unilever, Diageo and Procter & Gamble signed up to a new alliance with tech giants in a bid to improve brand safety.
Read more: Heinz and Unilever advertise on Pornhub, UK’s most popular adult website
The partnership, dubbed the Global Alliance for Responsible Media will lobby publishers and platforms to remove harmful and misleading content and “rapidly improve” digital safety.
But last week Unilever and Kraft Heinz faced criticism after it emerged they had advertised on Pornhub, the world’s largest adult website.
Main image credit: Getty


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STRING
Section: Linux Programmer's Manual (3)
Updated: 2014-01-04
Index
Return to Main Contents
NAME
stpcpy, strcasecmp, strcat, strchr, strcmp, strcoll, strcpy, strcspn,
strdup, strfry, strlen, strncat, strncmp, strncpy, strncasecmp, strpbrk,
strrchr, strsep, strspn, strstr, strtok, strxfrm, index, rindex
- string operations
SYNOPSIS
#include <strings.h>
- int strcasecmp(const char *s1, const char *s2);
-
Compare the strings
s1
and
s2
ignoring case.
- int strncasecmp(const char *s1, const char *s2, size_t n);
-
Compare the first
n
characters of the strings
s1
and
s2
ignoring case.
- char *index(const char *s, int c);
-
Return a pointer to the first occurrence of the character
c
in the string
s.
- char *rindex(const char *s, int c);
-
Return a pointer to the last occurrence of the character
c
in the string
s.
- #include <string.h>
-
- char *stpcpy(char *dest, const char *src);
-
Copy a string from
src
to
dest,
returning a pointer to the end of the resulting string at
dest.
- char *strcat(char *dest, const char *src);
-
Append the string
src
to the string
dest,
returning a pointer
dest.
- char *strchr(const char *s, int c);
-
Return a pointer to the first occurrence of the character
c
in the string
s.
- int strcmp(const char *s1, const char *s2);
-
Compare the strings
s1
with
s2.
- int strcoll(const char *s1, const char *s2);
-
Compare the strings
s1
with
s2
using the current locale.
- char *strcpy(char *dest, const char *src);
-
Copy the string
src
to
dest,
returning a pointer to the start of
dest.
- size_t strcspn(const char *s, const char *reject);
-
Calculate the length of the initial segment of the string
s
which does not contain any of bytes in the string
reject,
- char *strdup(const char *s);
-
Return a duplicate of the string
s
in memory allocated using
malloc(3).
- char *strfry(char *string);
-
Randomly swap the characters in
string.
- size_t strlen(const char *s);
-
Return the length of the string
s.
- char *strncat(char *dest, const char *src, size_t n);
-
Append at most
n
characters from the string
src
to the string
dest,
returning a pointer to
dest.
- int strncmp(const char *s1, const char *s2, size_t n);
-
Compare at most
n
bytes of the strings
s1
and
s2.
- char *strncpy(char *dest, const char *src, size_t n);
-
Copy at most
n
bytes from string
src
to
dest,
returning a pointer to the start of
dest.
- char *strpbrk(const char *s, const char *accept);
-
Return a pointer to the first occurrence in the string
s
of one of the bytes in the string
accept.
- char *strrchr(const char *s, int c);
-
Return a pointer to the last occurrence of the character
c
in the string
s.
- char *strsep(char **stringp, const char *delim);
-
Extract the initial token in
stringp
that is delimited by one of the bytes in
delim.
- size_t strspn(const char *s, const char *accept);
-
Calculate the length of the starting segment in the string
s
that consists entirely of bytes in
accept.
- char *strstr(const char *haystack, const char *needle);
-
Find the first occurrence of the substring
needle
in the string
haystack,
returning a pointer to the found substring.
- char *strtok(char *s, const char *delim);
-
Extract tokens from the string
s
that are delimited by one of the bytes in
delim.
- size_t strxfrm(char *dest, const char *src, size_t n);
-
Transforms
src
to the current locale and copies the first
n
characters to
dest.
DESCRIPTION
The string functions perform string operations on null-terminated
strings.
See the individual man pages for descriptions of each function.
SEE ALSO
index(3),
rindex(3),
stpcpy(3),
strcasecmp(3),
strcat(3),
strchr(3),
strcmp(3),
strcoll(3),
strcpy(3),
strcspn(3),
strdup(3),
strfry(3),
strlen(3),
strncasecmp(3),
strncat(3),
strncmp(3),
strncpy(3),
strpbrk(3),
strrchr(3),
strsep(3),
strspn(3),
strstr(3),
strtok(3),
strxfrm(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:56 GMT, September 18, 2014


This page tests the generic styling feature of basic web components. Generic
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Biography
Sushant Singh Rajput was born on January 21, 1986, to Krishna Kumar Singh and Usha Singh, in Purnia, Bihar. He did his schooling from St. Karen's High School in Patna and then from Kulachi Hansraj Model School in New Delhi. Sushant Singh Rajput was good at studies and cleared 11 engineering entrance examinations in his entire life. In 2003, he secured the seventh rank in the Delhi College of Engineering Entrance Exam and got admitted in B.E. Mechanical Engineering. Sushant was also a National Olympiad Winner in Physics. At Delhi College of Engineering, he actively participated in theatre. He drops out of college to pursue his acting career. More about Sushant Singh...
Most outstanding movies
- 2019
- Chhichhore
- Director: Nitesh Tiwari
- Character: Anni
- 2016
- MS Dhoni: The Untold Story
- Director: Neeraj Pandey
- Character: Mahendra Singh Dhoni
- 2018
- Kedarnath
- Director: Abhishek Kapoor
- Character: Mansoor Khan
- 2014
- PK
- Director: Rajkumar Hirani
- Character: Sarfraz Yousuf
- 2013
- Kai Po Che
- Director: Abhishek Kapoor
- Character: Ishaan Bhatt
- 2020
- Dil Bechara
- Director: Mukesh Chhabra
- Character: Manny
- 2017
- Raabta
- Director:Mukesh Chhabra
- Character: Shiv Kakkar
- 2013
- Shuddh Desi Romance
- Director: Maneesh Sharma
- Character: Raghu
Awards
(2010) Best Actor - Popular Pavitra Rishta
(2011) Best Actor - Popular Pavitra Rishta
(2017) Future of Fashion (Male)
(2016)Youth Icon Of The Year (Male)
(2011) Best Actor - Critics M.S. Dhoni: The Untold Story (2016)
(2014)Most Promising Debut Actor Kai po che! (2013)
(2015) Breakthrough Talent of the Year


Rocket League is seeing its biggest update yet this summer, and with it comes new content ranging from arenas to cars to a tidal wave of cosmetic options and more. Rocket League set the gaming community on fire when it launched last year, and the Neo Tokyo update shows that it has no signs of stopping.
The Neo Tokyo Arena
A lot of the personality of Rocket League comes from the stylistic design of its arenas. The Neo Tokyo arena has no shortage of this personality, with both its court and the surrounding region dripping in the vibrant lights and other signature features of a modern Japanese skyline. The arena’s layout even differs from the stadiums we’re used to in the game, with a concave center similar to one in the Rocket Labs experimental playlist.
The pillars of rocket league
Speaking of the Rocket Labs, the playlist now offers a new speciality map titled Pillars. True to its name, this arena features two massive beams in the center that stretch up to the ceiling, splitting the map into three separate lanes. Every map in the Rocket Labs playlist features a primary gimmick that flips the game’s strategy upside down, and Pillars’ pseudo-MOBA homage could definitely end up an interesting new twist.
A garage full
Neo Tokyo’s new machines aren’t launching for the game until a couple weeks after the rest of the update, but they look worth the wait. There are four new vehicles coming in the summer update: the first two, titled Esper and Masamune, boast the Tokyo style of their host update, while the other two, Aftershock and Marauder, are based on more classic Rocket League designs. All four cars will be available to players starting July 18th, with six unique decals offered for each.
Everything else
While Rocket League’s summer update brings with it some major additions, there are plenty of smaller tweaks and bonuses fans can take advantage of. As is customary with Rocket League, content updates are filled to the brim with new car cosmetics like antennas, rocket trails, and more. Other perks include the new Showroom (where players can show off their goods), an added trade system for some player synergy, a new Quick Chat feature for improved player expression, and more.


Blackpool weekend weather forecast: This is when heavy rain will hit
The weather in Blackpool this weekend is set to be dull, with heavy rain and cloud.
by Helen JohnsonHere’s what to expect.
Friday (22 Nov)
Friday will see overcast conditions throughout most of the day. Light rain will hit between 4pm and 5pm, before turning cloudy again.
Light rain will then return from 8pm onwards, turning heavier at 9pm, but easing off by around 10pm. Overnight temperature of 7C.
Saturday (23 Nov)
Saturday will see rain hit throughout the day. Light rain will turn heavy by around 9am.
This will turn lighter by the afternoon, but continue to hit throughout the rest of the day and evening. The temperature will reach its peak of 9C by 9am and remain so throughout the day.
Overnight temperature of 8C.
Sunday (24 Nov)
Sunday is then set to be cloudy throughout the day, with the temperature reaching its peak of 9C by 12pm.
The temperature will then begin to dip after 3pm, with an overnight temperature of 8C.
The Met Office forecast for the North West for Sunday to Tuesday explains that it will be “Drier for much of Sunday before more rain spreads in from the west, accompanied by strengthening winds.
“Mainly cloudy thereafter with scattered showers. Fairly mild.”


The Beta Kappa chapter of Lambda Chi Alpha was established in 1942, but has much deeper roots within the Georgia Tech community.
Established with the purpose of recruiting the finest gentlemen and guiding them on their journey through university, Beta Kappa has evolved into the premier fraternity on campus, providing a Lifetime of Brotherhood to over 2000 gentlemen. If you'd like to learn about the 75 years of history in the Beta Kappa chapter, click
here.
Every year Lambda Chi Alpha collects hundreds of thousands of pounds of cans for Feeding America. As a national fraternity, we have raised over 10 million pounds since 2013. Within our local chapter, we participate in the annual St. Baldricks competition which benefits childhood cancer research. To find out more about our philanthropy, click here.
Lambda Chi Alpha provides multiple scholarships ranging anywhere up to $2000 for incoming gentlemen.
Check out the Beta Kappa Scholarship for incoming gentlemen
here.


Introduction
In fact, “semantic parsing” is, ironically, a semantically ambiguous term, which could refer to:
- Semantic role labeling (also known as shallow semantic parsing, see previous post for more information).
- Finding generic relations in text.
- Transforming a natural language sentence into its meaning representation.
In this post, semantic parsing is the task of mapping natural language sentences (NL) into complete formal meaning representation (MRs) which a computer can execute for some domain -specific application 1. Representing the meaning of natural language is ultimately a difficult philosophical question and many attempts have been made to define generic formal semantics of natural language, including:
-
Abstract Meaning Representation (AMR)
-
Semantic Dependency Parsing (SDP)
-
Meaning Representation Language (MRL)
-
SQL Query
Abstract Meaning Representation
Abstract Meaning Representation (AMR) 2 is a semantic formalism in which the meaning of a sentence is encoded as a rooted, directed, acyclic graph. Nodes represent concepts, and labeled directed graph edges represent the relationships between them. For example, for the sentence “the boy wants to believe the girl” we can have the corresponding AMR graph (in penman format) as:
(w / want-01
:ARG0 (b / boy)
:ARG1 (b2 / believe-01
:ARG0 (g / girl)
:ARG1 b))
AMR parsing, the task of transforming a sentence into its AMR graph, is a challenging task as it requires the parser to learn to predict not only concepts, which consist of predicates, lemmas, named entities, wiki-links and co-references, but also a large number of relation types based on the semantic role in PropBank (see the previous blog Semantic Role Labeling if you are interested).
Data
There are multiple version of AMR data, including:
-
LDC2013E117: 10,853 sentences with 13,050 AMRs (corpus includes multiple annotations for some sentences)
-
LDC2014T12 (AMR 1.0): 13,051 sentences.
-
LDC2015E86: 19,572 sentences. This version includes more AMRs, wikification, AMR adoption of new unified PropBank frames, AMR deepening, automatic AMR-English alignments, and correction of AMR annotation errors.
-
LDC2016E25: 39,260 sentences.
-
LDC2017T10 (AMR 2.0): 39,260 sentences.
Experiment & Results
These are the AMR parsing results on LDC2014T12, LDC2015E86 and LDC2017T10 test sets, where 2014N refers to the newswire section of LDC2014T12. You should look the original paper if you are interested in the model. Please contact me (through email) if the statistics is incorrect.
| Paper | Model | LDC2014N | LDC2014 | LDC2015 | LDC2017 |
|---|---|---|---|---|---|
| ACL-18 | Lyu & Titov University of Edinburgh & University of Amsterdam |
- | - | 73.7 | 74.4 |
| ACL-18 | Groschwitz et al. Saarland University & Macquerie University |
- | - | 70.2 | 71.0 |
| ACL-17 | Buys & Blunsom University of Oxford & Deepmind |
- | - | - | 61.9 |
| ACL-17 | Foland & Martin University of Colorado |
- | - | 70.7 | - |
| EMNLP-18 | Guo & Lu Singapore University of Technology & Design |
74.0 | 68.3 | 68.7 | 69.8 |
| Arxiv-17 | van Noord & Bos University of Groningen |
- | - | 68.5 | 71.0 |
| EMNLP-17 | Wang & Xue Brandeis University |
- | 68.1 | 68.1 | - |
| Semeval-16 | Wang et al. (CAMR) Brandeis University & Boulder Language Technologies |
- | 66.5 | 67.3 | - |
| Semeval-16 | Barzdins & Gosko University of Latvia |
- | - | 67.2 | - |
| Semeval-16 | Flanigan et al. (JAMR) Carnegie Mellon University & University of Washington |
- | 66 | 67 | - |
| EACL-17 | Damonte et al. University of Edinburgh & University of Padua |
- | 64 | 64 | - |
| NAACL-18 | Vilares & Gómez-Rodríguez Universidade da Coruña |
- | - | 64 | - |
| AAAI-18 | Peng et al. University of Rochester & University of Padua |
- | - | 64 | - |
| ACL-17 | Konstas et al. University of Washington & Allen Institute for Artificial Intelligence |
- | - | 62.1 | - |
| EACL-17 | Peng et al. University of Rochester & Brandeis University |
- | - | 52 | - |
| EMNLP-15 | Pust et al. Univerisity of Southern California |
- | 67.1 | - | - |
| EMNLP-16 | Zhou et al. Nianjin Normal University & DFKI, Germany |
71 | 66 | - | - |
| Semeval-16 | Goodman et al. University College London & University of Sheffeld |
70 | - | 64 | - |
| ACL-IJCNLP-15 | Wang et al. (CAMR) Brandeis University & Boulder Language Technologies |
70 | 66 | - | - |
| EMNLP-17 | Ballesteros & AI-Onaizan IBM T.J Waston Research Center |
69 | 64 | - | - |
| EMNLP-15 | Artzi et al. Cornell University & University of Washington |
67 | - | - | - |
| ACL-IJCNLP-15 | Werling et al. Stanford University |
62 | - | - | - |
| NAACL-15 | Wang et al. (CAMR) Brandeis University & Harvard Medical School |
- | 59 | - | - |
| ACL-14 | Flanigan et al. (JAMR) Carnegie Mellon University |
59 | 58 | - | - |
| CoNLL-15 | Peng et al. University of Rochester |
58 | - | - | - |
Semantic Dependency Parsing
Task 8 at SemEval 2014 defines Broad-Coverage Semantic Dependency Parsing (SDP) as the problem of recovering sentence-internal predicate-argument relationships for all content words, i.e., the semantic structure constituting the relational core of sentence meaning 3.
SDP seeks to stimulate the dependency parsing community to move towards more general graph processing, to thus enable a more direct analysis of Who did What to Whom? In addition to its relation to syntactic dependency parsing, the task also has some overlap with Semantic Role Labeling (SRL) 4.
The SDP parsers are required to identify all semantic dependencies, i.e., compute a representation that integrates all content words in one structure. Different from SRL, SDP task does not encompass predicate disambiguation, a design decision in part owed to SDP’s goal to focus on parsing-oriented, i.e., structural, analysis, and in part to lacking consensus on sense inventories for all content words.
SDP uses three distinct target representations for semantic dependencies. Including:
DM: DELPH-IN MRS-Derived Bi-Lexical Dependencies
These semantic dependency graphs originate in a manual re-annotation of Sections 00–21 of the WSJ Corpus with syntactico-semantic analyses derived from the LinGO English Resource Grammar (ERG) 5. The DM target representations are derived through a two-step ‘lossy’ conversion of MRSs, first to variable-free Elementary Dependency Structures (EDS) 6, then to ‘pure’ bi-lexical form - projecting some construction semantics onto word-to-word dependencies 7. For example, for the sentence “Ms. Haag plays Elianti.”, we have the DM representation as follows:
| id | form | lemma | pos | top | pred | arg1 | arg2 |
|---|---|---|---|---|---|---|---|
| 1 | Ms. | Ms. | NNP | - | + | _ | _ |
| 2 | Haag | Haag | NNP | - | - | compound | ARG1 |
| 3 | plays | play | VBZ | + | + | _ | _ |
| 4 | Elianti | Elianti | NNP | - | - | _ | ARG2 |
| 5 | . | . | . | - | - | _ | _ |
PAS: Enju Predicate-Argument Structures
The Enju parsing system is an HPSG-based parser for English. The grammar and the disambiguation model of this parser are derived from the Enju HPSG treebank, which is automatically converted from the phrase structure and predicate-argument structure annotation of the PTB. The PAS data set is extracted from the WSJ portion of the Enju HPSG treebank. While the Enju treebank is annotated with full HPSG-style structures, only its predicate-argument structures are convert into the SDP data format for use in the SDP task.
PCEDT: Prague Tectogrammatical Bi-Lexical Dependencies
The Prague Czech-English Dependency TreeBank (PCEDT) 8 is a set of parallel dependency trees over the WSJ texts from the PTB, and their Czech translations. The specifics of the PCEDT representations are best observed in the procedure that converts the original PCEDT data to the SDP data format; see Miyao et al. 2014 9.
Data
-
Semeval-2014 Task 8: For this task, there will be three data sets: training, development, and test. On November 4, 2013, a sample (of some 190 dependency graphs) from the training data was published as trial data, demonstrating key characteristics of the task. Since December 13, some 750,000 tokens of annotated text are available as training data; please subscribe to the task mailing list for access information.
-
Semeval-2015 Task 8: This Task is a re-run with some extensions of Task 8 at SemEval-2014.
Experiment & Results
The results are the labeled parsing performance (F1 score) on both in-domain and out-of-domain test data and are based on the English dataset from SemEval 2015 Task 18 closed track. The data split is 33,964 training sentences from 00-19 of the WSJ corpus, 1,692 development sentences from 20, 1,410 sentences from 21 as in domain test data, and 1,849 sentences sampled from the Brown Corpus as out-of-domain test data.
In-Domain
| paper | model | DM | PAS | PSD |
|---|---|---|---|---|
| ACL-2018 | Dozat & Manning Stanford University |
93.7 | 93.9 | 81.0 |
| ACL-2017 | Peng et al. University of Washington & Carnegie Mellon University |
90.4 | 92.7 | 78.5 |
| AAAI-2018 | Wang et al. Harbin Institute of Technology |
90.3 | 91.7 | 78.6 |
| Semeval-2015 | Du et al. Peking University |
89.1 | 91.3 | 75.7 |
| Semeval-2015 | Almeida & Martins Priberam Labs & Instituto de Telecomunicacoes |
88.2 | 90.9 | 76.4 |
Out-Of-Domain
| paper | model | DM | PAS | PSD |
|---|---|---|---|---|
| ACL-2018 | Dozat & Manning Stanford University |
88.9 | 90.6 | 79.4 |
| ACL-2017 | Peng et al. University of Washington & Carnegie Mellon University |
85.3 | 89.0 | 76.4 |
| AAAI-2018 | Wang et al. Harbin Institute of Technology |
84.9 | 87.6 | 75.9 |
| Semeval-2015 | Du et al. Peking University |
81.8 | 87.2 | 73.3 |
| Semeval-2015 | Almeida & Martins Priberam Labs & Instituto de Telecomunicacoes |
81.8 | 86.9 | 74.8 |
Logical Form Representation
First-order logic is used as a semantic representation language (MRL), which is designed by the creator of the application to suit the application’s need independent of natural language. Some MRL data sets are based on Prolog database, where two well-known semantic parsing benchmark data sets are Jobs and Geo 10.
Here we explain the features of the Geoquery representation language through a sample query:
Input: "What is the largest city in Texas?"
Query: answer(C, largest(C, (city(C), loc(C, S), const(S, stateid(texas)))))
Objects are represented as logical terms and are typed with a semantic category using logical functions applied to possibly ambiguous English constants (e.g. stateid(Mississippi), riverid(Mississippi)
). Relationships between objects are expressed using predicates; for instance, loc(X, Y)
states that X
is located in Y
.
We also need to handle quantifiers such as ‘largest’. We represent these using meta-predicates for which at least one argument is a conjunction of literals. For example, largest(X, Goal)
states that the object X
satisfies Goal
and is the largest object that does so, using the appropriate measure of size for objects of its type (e.g. area for states, population for cities). Finally, an unspecified object required as an argument to a predicate can appear elsewhere in the sentence, requiring the use of the predicate const(X, C)
to bind the variable X
to the constant C
.
Data
-
Geo 880: A set of 880 queries to a database of U.S. geography containing basic information about the U.S. states like population, area, capital city, neighboring states, and so on. It is originally annotated with Prolog style semantics. Geo has 880 instances split into a training set of 680 training examples and 200 test examples 11.
-
Jobs 640: A set of 640 queries to a database of computer-related job postings, such as job announcements, from the USENET newsgroup
austin.jobs
. Information from these job postings are extracted to create a database which contains the following types of information: 1) the job title, 2) the company, 3) the recruiter, 4) the location, 5) the salary, 6) the languages and platforms used, and 7) required or desired years of experience and degrees. The training-test split contains 500 training and 140 test instances. -
ATIS: A set of 5,410 queries to a flight booking system, and the representations are of lambda-calculus logical forms. The ATIS database consists of data obtained from the Official Airline Guide (OAG, 1990), organized under a relational schema. The database remained fixed throughout the pilot phase. It contains information about flights, fares, airlines, cities, airports, and ground services, and includes twenty-five supporting tables. The large majority of the questions posed by subjects can be answered from the database with a single relational query.
Experiment & Results
The results are based on accuracies which are defined as the proportion of the input sentences that are correctly parsed to their gold standard logical forms.
| paper | model | Geo880 | Jobs640 | ATIS |
|---|---|---|---|---|
| EMNLP-18 | Xu et al. Tencent AI Lab & IBM Research & Peking University |
88.1 | 91.2 | 85.9 |
| ACL-17 | Rabinovich et al. UC Berkeley |
85.7 | 92.9 | 85.3 |
| ACL-11 | Liang et al. UC Berkeley |
87.9 | 90.7 | - |
| ACL-14 | Wang et al. University of Washington & Allen Institute for AI |
90.4 | - | 91.3 |
| EMNLP-13 | Kwiatkowski et al. University of Washington |
89.0 | - | - |
| NAACL-15 | Zhao and Huang City University of New York |
88.9 | 85.0 | 84.2 |
| ACL-13 | Poon Microsoft Research |
- | - | 83.5 |
| EMNLP-11 | Kwiatkowski et al. University of Edinburgh & University of Washington |
88.6 | - | 82.8 |
| EMNLP-10 | Kwiatkowski et al. University of Edinburgh & University of Washington |
87.9 | - | 71.4 |
| ACL-16 | Dong & Lapata -Seq2Tree University of Edinburgh |
87.1 | 90.0 | 85.3 |
| ACL-07 | Wong and Mooney University of Texas |
86.6 | - | - |
| EMNLP-CoNLL-07 | Zettlemoyer and Collins MIT |
86.1 | - | 84.6 |
| ACL-16 | Dong & Lapata -Seq2Seq University of Edinburgh |
85.0 | 87.1 | 84.2 |
| ACL-16 | Jia & Liang Stanford University |
85.0 | - | 76.3 |
| EMNLP-08 | Lu et al. Singapore-MIT alliance & National University of Singapore & MIT |
81.8 | - | - |
| ECML-01 | Tang & Mooney University of Texas |
79.8 | 79.4 | - |
| UAI-05 | Zettlemoyer and Collins MIT |
79.3 | 79.3 | - |
| IUI-03 | Poposcu et al. University of Washington |
77.5 | 88.0 | - |
| NAACL-06 | Wong and Mooney University of Texas |
74.8 | - | - |
| CoNLL-05 | Ge and Mooney University of Texas |
72.3 | - | - |
| COLING-ACL-06 | Kate and Mooney University of Texas |
71.7 | - | - |
SQL Query
In recent years, there has been considerable interest in automated synthesis of programs from informal specifications. A particularly promising application domain for such computer-aided programming techniques is the automated synthesis of database queries. Although many end-users need to query data stored in some relational database, they typically lack the expertise to write complex queries in declarative query languages such as SQL. As a result, there has been a flurry of interest in automatically synthesizing SQL queries from informal specifications. Here we will talk about the techniques that can generate SQL queries from natural language (NL) descriptions.
For example, for the sentence “how many CFL teams are from York College”, we will have the SRL query like:
SELECT COUNT CFL Team FROM
CFLDraft WHERE College = "York"
Data
- WikiSQL: WikiSQL is a collection of questions, corresponding SQL queries, and SQL tables. It is the largest hand-annotated semantic parsing dataset to data - it is an order of magnitude larger than other datasets that have logical forms, either in terms of the number of examples or the number of tables. The corpus consists of 80654 hand-annotated instances of natural language questions, SQL queries, and SQL tables extracted from 24241 HTML tables from Wikipedia 12.
Experiment and Results
To learn more about the experiment and results on WikiSQL, see https://github.com/salesforce/WikiSQL for more details.
-
Rohit J Kate and YukWahWong. 2010. Semantic parsing: The task, the state of the art and the future. Tutorial Abstracts of ACL 2010 page 6. ↩
-
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. Abstract meaning representation for SemBanking. In Proc. of the 7th Linguistic Annotation Workshop and Interoperability with Discourse. ↩
-
Oepen, S., Kuhlmann, M., Miyao, Y., Zeman, D., Flickinger, D., Hajic, J., Ivanova, Angelina, Zhang, Y. 2014. SemEval 2014 Task 8: Broad-coverage semantic dependency parsing. In Proceedings of the 8th International Workshop on Semantic Evaluation. Dublin, Ireland. ↩
-
Gildea, D., & Jurafsky, D. 2002. Automatic labeling of semantic roles. Computational Linguistics, 28, 71:245–288. ↩
-
Flickinger, D., Zhang, Y., & Kordoni, V. 2012. DeepBank. A dynamically annotated treebank of the Wall Street Journal. In Proceedings of the 11th International Workshop on Treebanks and Linguistic Theories (p. 85–96). Lisbon, Portugal: Edições Colibri. ↩
-
Oepen, S., & Lønning, J. T. 2006. Discriminantbased MRS banking. In Proceedings of the 5th International Conference on Language Resources and Evaluation (p. 1250–1255). Genoa, Italy. ↩
-
Ivanova, A., Oepen, S., Øvrelid, L., & Flickinger, D. 2012. Who did what to whom? A contrastive study of syntacto-semantic dependencies. In Proceedings of the Sixth Linguistic Annotation Workshop (p. 2–11). Jeju, Republic of Korea. ↩
-
Hajic, J., Hajicová, E., Panevová, J., Sgall, P., Bojar, O., Cinková, S., … & Semecký, J. 2012. Announcing Prague Czech-English Dependency Treebank 2.0. In LREC (pp. 3153-3160). ↩
-
Miyao, Y., Oepen, S., & Zeman, D. 2014. In-house: An ensemble of pre-existing off-the-shelf parsers. In Proceedings of the 8th International Workshop on Semantic Evaluation. Dublin, Ireland. ↩
-
Lappoon R. Tang and Raymond J. Mooney. 2001. Using multiple clause constructors in inductive logic programming for semantic parsing. In Proceedings of the 12th ECML, pages 466–477, Freiburg, Germany. ↩
-
Luke S. Zettlemoyer and Michael Collins. 2005. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Proceedings of the 21st UAI, pages 658–666, Toronto, ON. ↩
-
Zhong, V., Xiong, C. and Socher, R., 2017. Seq2SQL: Generating structured queries from natural language using reinforcement learning. arXiv preprint arXiv:1709.00103. ↩


Professional Background
- Founder and COO at SoonSpoon
- Provided last minute restaurant reservations to hungry diners
- Hosted pop-ups to highlight local chefs and engage the user community
- Acquired by Reserve Media
- Founder and COO at Rainbow Chronicle which became OutGrade
- A ratings site highlighting LGBT friendly areas in your town and across the world
- Ratings collected from all 50 states and 18 countries
- Recognized by Cambridge, MA City Council as a valuable asset.


Classes
The following classes are available globally.
-
StitchGCMPushClient is the PushClient for GCM. It handles the logic of registering and deregistering with both GCM and Stitch.
It does not actively handle updates to the Instance ID when it is refreshed.
See moreDeclaration
Swift
public class StitchGCMPushClient: PushClient
-
Undocumented
See moreDeclaration
Swift
public final class StitchClientFactory: StitchClientFactoryProtocol
-
Undocumented
See moreDeclaration
Swift
public class StitchClient: StitchClientType
-
AvailablePushProviders is a collection of available push providers for an app and the information needed to utilize them.
See moreDeclaration
Swift
public class AvailablePushProviders: Codable
-
Simple data holder for push messages with Stitch formatting.
See moreDeclaration
Swift
@objc public class PushMessage: NSObject
-
Undocumented
Declaration
Swift
public class TwilioService: Service
-
PushManager is responsible for handling the creation of PushClient while handling any events from the [StitchClient] that require changes to the clients.
See moreDeclaration
Swift
public class PushManager: AuthDelegate
-
Stitch GCMPushProviderInfo contains information needed to create a
See moreStitchGCMPushClient
.Declaration
Swift
public class StitchGCMPushProviderInfo: PushProviderInfo
-
Undocumented
See moreDeclaration
Swift
public class StitchNetworkAdapter: NetworkAdapter
-
Helper class to construct PushProviderInfo from persistent storage
See moreDeclaration
Swift
public class PushProviderInfoHelper


Sunrise is a free calendar app specifically designed to make your life easier. All to-dos with a due date get synced to a calendar in Sunrise.
Unfortunitly the integrations you're looking for doesn't exist


---
layout: post
title: "My Week in Review"
date: 2015-06-28 10:45:00 +0000
categories:
tags:
image: https://s3.amazonaws.com/audreywatters/2015-06-28-rainbow.jpg
imagecredits: https://www.flickr.com/photos/surreal_badger/18994282129/
---
Travel
- Hermosa Beach, then Philadelphia.
- Upcoming week: Philadelphia, then Hermosa Beach.
Hack Education
Essays Elsewhere
Speaking
Known Notes and Fragments
Other Projects
- I had several responses to my article on Sesame Street (here and here), and hit Stephen Downes’ OLDaily twice this week.
- I had to go the doctor for follow-up on some tests. Whee.
Viewing
- I’ve been watching the Women’s World Cup.
Family and Friends
- It was Kin’s birthday on the 24th. It was my Granny’s as well.
- She passed away this week.
- One of the joys of being at ISTE is seeing friends. And after recent events, I have needed that.


Banking System
Home
Customer's Portal
welcome
Our Services
Choose to View Customers or to see Transaction history
View Customers and Transactions
Transaction History
Customer Portal
View Customers and perform Transactions
Transaction History


Walking with Grace is a charity set up in remembrance of our daughter Grace who sadly past away on the 24th of June 2015 after 47 amazing days with us. The aim of our charity is to support families with babies requiring Intensive Care or High Dependancy Incubated Neonatal Care; initially for babies born at North Tees University Hospital with the gift of a Cuski Miniboo like the pink one seen in Graces picture. The mini comforter aid in the bonding between parents and babies in incubators and are NHS approved.
Tomorrow see the start of Orange Bus Charity Week Read More ›


| Home | Trees | Index | Help |
|---|
| Package svnmailer :: Module processes :: Class ProcessImpl |
|
Popen3
--+
|
ProcessImpl
| Method Summary | |
|---|---|
Initialization | |
| int |
Waits for child process to terminate. |
| Inherited from Popen3 | |
Return the exit status of the child process if it has finished, or -1 if it hasn't finished yet. | |
|
| Instance Variable Summary | |
|---|---|
| file | fromchild : pipe from the child |
| file | tochild : pipe to the child |
| Class Variable Summary | |
|---|---|
| bool | WINSHELL : Does the implementation use a shell on Win32? |
| Inherited from Popen3 | |
int |
sts = -1 |
| Method Details |
|---|
__init__(self,
cmd,
pstdin,
pstdout,
pstderr,
detach)
Initialization |
wait(self)Waits for child process to terminate. The actual wait() call may happen when the last pipe handle is closed (in the dumb implementation).
|
| Instance Variable Details |
|---|
fromchildpipe from the child
|
tochildpipe to the child
|
| Class Variable Details |
|---|
WINSHELLDoes the implementation use a shell on Win32?
|
| Home | Trees | Index | Help |
|---|
| Generated by Epydoc 2.0 on Sun Sep 25 20:49:06 2005 | http://epydoc.sf.net |


Third lesson day
February 8 2007, 10:56 PM
Today was y first light day.
With the agreement of Bonaventure, the rector of the university, I've reduced my charge to 6 hours a day.
And actually, I did almost 7 hours today since I've also reduced the Tea-break (which by the way has tea only in its name!) to 15 mn instead of half an hour and I've finished always later than what I should.
But still, it's incredible the difference between 7 hours a day, and 8 hours when you have to give class. I feel much more better after my day than the previous days.
During the morning I had only 12 students (the 9 of the third class are not attending anymore the database 1 class). We continued on the operation of the algebra on the relations. There are 3 students that have a much better level than the others. Hence, it's quite difficult for me to be manage them. They may think that I'm a bit too slow, but on the other hand, the others have really difficulties to understand the matter.
We stop at 11:20 instead of 12 (since I had to give them only 3 hours, instead of 4). But since we also reduced the T-break, at the end, it's was quite the same.
I've started the afternoon at 3 instead of 2. That way I can make a tiny nap. Just enough to be much more relaxed. Perfect.
I gave class till 6:10 with a reduced break too.
We continued the HTML introduction. I started the class by recording them what we do, and why we do it. Why HTML will be useful for them in order to achieve their project.
Since it's not the topic of the course, I've just limited this introduction to the things they really need to know.
So, today, we have just seen the lists and the forms.
In parallel, they have to finish their first report on their own project (one topic per group) by tomorrow. It consists of a description and a first version of the entity-relations graph. So tomorrow, we'll start to learn how to convert it to a relational graph. That way, next week we will be able to start implementing the database in MySQL. By the end of the week, I hope I will start the introduction to PHP and they will have all the tools to implement a real simple program that will interact with their own homemade database.
The afternoon passed very well. I wrote a lot on the black board and I felt very more and more comfortable with my way of giving class. And this is very important to me.
After the class, we, Bénédicte, Julien (both from France), Augustin (from the university) and I went to a bar on the side of the (small) water-pool of Ngozi. We had very good time. Then, we went back to the "Paroisse" for supper and once again, we got (free) beers and we had very nice chats.
At the end, it was a very pleasant day. A subtle balance between work and fun. A perfect day! :-)
It's only 22:56 and it's already time to sleep. Days begin very early over here.


The Adventures of Jerry Muskrat
by Thornton W. Burgess
Chapter 25: “Paddy the Beaver Decides to Stay”
Additional Information
- Year Published: 1914
- Language: English
- Country of Origin: United States of America
- Source: Burgess, T.W. (1914). The Adventures of Jerry Muskrat.
-
Readability:
- Flesch–Kincaid Level: 5.5
- Word Count: 774
- Genre: Fantasy
- ✎ Cite This
Downloads
”The fair Green Meadows spreading wide,
The Smiling Pool and Laughing Brook—
They fill our hearts with joy and pride;
We love their every hidden nook.”
A muskratSo said Jerry Muskrat, as he climbed up on the Big Rock in the middle of the Smiling Pool, with Paddy the Beaver beside him, and watched the dear Smiling Pool dimpling and smiling in the moonlight, as he had so often seen it before the great trouble had come.
“Chugarum!” said Grandfather Frog in his great deep voice from the bulrushes. “One never knows how great their blessings are until they have been lost and found again.”
The bulrushes nodded, as if they too were thinking of this. You see their feet were once more in the cool water. Paddy the Beaver seemed to understand just how every one felt, and he smiled to himself as he saw how happy these new friends of his were.
“It surely is a very nice place here, and I don’t wonder that you couldn’t bear to leave it,” said he. “I’m sorry that I made you all that trouble and worry, but you see I didn’t know.”
“Oh, that’s all right,” replied Jerry Muskrat, who was now very proud of his big cousin. “I hope that now you see how nice it is, you will stay and make your home here.”
Paddy the Beaver looked back at the great black shadow which he knew was the Green Forest. Way over in the middle of it he heard the hunting-call of Hooty the Owl. Then he looked out over the Green Meadows, and from way over on the far side of them sounded the bark of Reddy Fox, and it was answered by the deep voice of Bowser the Hound up in Farmer Brown’s dooryard. For some reason that last sound made Paddy the Beaver shiver a little, just as the voice of Hooty the Owl made the smaller people of the Green Forest and the Green Meadows shiver when they heard it. Paddy wasn’t afraid of Hooty or of Reddy Fox, but Bowser’s great voice was new to him, and somehow the very sound of it made him afraid. You see, the Green Meadows were so strange and open that he didn’t feel at all at home, for he dearly loves the deepest part of the Green Forest.
“No,” said Paddy the Beaver, “I can’t possibly live here in the Smiling Pool. It is a very nice pool, but it wouldn’t do at all for me, Cousin Jerry. I wouldn’t feel safe here a minute. Besides, there is nothing to eat here.”
“Oh, yes, there is,” Jerry Muskrat interrupted. “There are lily-roots and the nicest fresh-water clams and—”
“But there are no trees,” said Paddy the Beaver, “and you know I have to have trees.”
Jerry stared at Paddy as if he didn’t understand. “Do—do you eat trees?” he asked finally.
Paddy laughed. “Just the bark,” said he, “and I have to have a great deal of it.”
Jerry looked as disappointed as he felt. “Of course you can’t stay then,” said he, “and—and I had thought that we would have such good times together.”
Paddy’s eyes twinkled. “Perhaps we may yet,” said he. “You see I have about made up my mind that I will stay a while along the Laughing Brook in the Green Forest, and you can come to see me there. On our way down I saw a very nice hole in the bank that I think will make me a good house for the present, and you can come up there to see me. But if I do stay, you and Grandfather Frog and Spotty the Turtle must keep my secret. No one must know that I am there. Will you?”
“Of course we will!” cried Jerry Muskrat and Grandfather Frog and Spotty the Turtle together.
“Then I’ll stay,” said Paddy the Beaver, diving into the Smiling Pool with a great splash.
And so one of Jerry Muskrat’s greatest adventures ended in the finding of his biggest cousin, Paddy the Beaver.


5.2. Binary Trees (WORK IN PROGRESS)¶
5.2.1. Definitions and Properties¶
A binary tree is made up of a finite set of elements called nodes. This set either is empty or consists of a node called the root together with two binary trees, called the left and right subtrees, which are disjoint from each other and from the root. (Disjoint means that they have no nodes in common.) The roots of these subtrees are children of the root. There is an edge from a node to each of its children, and a node is said to be the parent of its children.
If \(n_1, n_2, ..., n_k\) is a sequence of nodes in the tree such that \(n_i\) is the parent of \(n_i+1\) for \(1 \leq i < k\), then this sequence is called a path from \(n_1\) to \(n_k\). The length of the path is \(k-1\). If there is a path from node \(R\) to node \(M\), then \(R\) is an ancestor of \(M\), and \(M\) is a descendant of \(R\). Thus, all nodes in the tree are descendants of the root of the tree, while the root is the ancestor of all nodes. The depth of a node \(M\) in the tree is the length of the path from the root of the tree to \(M\). The height of a tree is the depth of the deepest node in the tree. All nodes of depth \(d\) are at level \(d\) in the tree. The root is the only node at level 0, and its depth is 0. A leaf node is any node that has two empty children. An internal node is any node that has at least one non-empty child.
Figure 5.3.1 illustrates the various terms used to identify parts of a binary tree. Figure 5.3.2 illustrates an important point regarding the structure of binary trees. Because all binary tree nodes have two children (one or both of which might be empty), the two binary trees of Figure 5.3.2 are not the same.
Two restricted forms of binary tree are sufficiently important to warrant special names. Each node in a full binary tree is either (1) an internal node with exactly two non-empty children or (2) a leaf. A complete binary tree has a restricted shape obtained by starting at the root and filling the tree by levels from left to right. In the complete binary tree of height \(d\), all levels except possibly level \(d\) are completely full. The bottom level has its nodes filled in from the left side.
Figure 5.3.3 illustrates the differences between full and complete binary trees. 1 There is no particular relationship between these two tree shapes; that is, the tree of Figure 5.3.3 (a) is full but not complete while the tree of Figure 5.3.3 (b) is complete but not full. The heap data structure is an example of a complete binary tree. The Huffman coding tree is an example of a full binary tree.
- 1
While these definitions for full and complete binary tree are the ones most commonly used, they are not universal. Because the common meaning of the words “full” and “complete” are quite similar, there is little that you can do to distinguish between them other than to memorize the definitions. Here is a memory aid that you might find useful: “Complete” is a wider word than “full”, and complete binary trees tend to be wider than full binary trees because each level of a complete binary tree is as wide as possible.


Locker
A quote from https://github.com/wooga/locker:
locker
is a distributed de-centralized consistent in-memory key-value store written in Erlang. An entry expires after a certain amount of time, unless the lease is extended. This makes it a good practical option for locks, mutexes and leader election in a distributed system.
Locker
is a Elixir wrapper for the locker
Erlang library that provides some useful libraries that should make using locker
a bit easier.
The Locker
module implements an OTP application that runs locker
. In addition to that is also provides a wrapper for the locker
API, just for convenience.
Idea of Locker
is to provide an abstration for globally registering GenServer
or :gen_fsm
processes to :locker
key-value store in a cluster of Erlang servers. These are implemented in Locker.Server
and Locker.Fsm
modules. In the simplest case, all you have to do is replace use GenServer
with use Locker.Server
and your process is automatically registered to global :locker
cluster.


anki_vector.faces¶
Face recognition and enrollment.
Vector is capable of recognizing human faces, tracking their position and rotation (“pose”) and assigning names to them via an enrollment process.
The anki_vector.world.World
object keeps track of faces the robot currently
knows about, along with those that are currently visible to the camera.
Each face is assigned a Face
object, which generates a number of
observable events whenever the face is observed, has its ID updated.
Classes
Expression |
Facial expressions that Vector can distinguish. |
Face (robot, pose, image_rect, face_id, name, …) |
A single face that Vector has detected. |
FaceComponent (robot) |
Manage the state of the faces on the robot. |
-
class
anki_vector.faces.
Expression
¶ Facial expressions that Vector can distinguish.
Facial expression not recognized. Call
anki_vector.robot.Robot.vision.enable_face_detection(detect_faces=True)()
to enable recognition.-
ANGER
= 4¶ Facial expression anger
-
HAPPINESS
= 2¶ Facial expression happiness
-
NEUTRAL
= 1¶ Facial expression neutral
-
SADNESS
= 5¶ Facial expression sadness
-
SURPRISE
= 3¶ Facial expression surprise
-
-
class
anki_vector.faces.

May represent a face that has previously been enrolled, in which case
name
will hold the name that it was enrolled with.Each Face instance has a
face_id
integer - This may change if Vector later gets an improved view and makes a different prediction about which face he is looking at.-
expression
¶ The facial expression Vector has recognized on the face.
Will be
Expression.UNKNOWN
by default if you haven’t calledanki_vector.robot.Robot.vision.enable_face_detection(detect_faces=True, estimate_emotion=True)()
to enable the facial expression estimation. Otherwise it will be equal to one of:Expression.NEUTRAL
,Expression.HAPPINESS
,Expression.SURPRISE
,Expression.ANGER
, orExpression.SADNESS
.import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"expression: {face.expression}")
Return type: str
-
expression_score
¶ The score/confidence that
expression
was correct.Will be 0 if expression is
Expression.UNKNOWN
(e.g. ifanki_vector.robot.Robot.vision.enable_face_detection(detect_faces=True, estimate_emotion=True)()
wasn’t called yet). The maximum possible score is 100.import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"expression_score: {face.expression_score}")
Return type: List
[int
]
-
face_id
¶ The internal ID assigned to the face.
This value can only be assigned once as it is static in the engine.
Getter: Returns the face ID Setter: Sets the face ID import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: # Print the visible face ids for face in robot.world.visible_faces: print(f"Visible id: {face.face_id}")
Return type: int
-
has_updated_face_id
¶ True if this face been updated / superseded by a face with a new ID.
import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: was_face_originally_unrecognized_but_is_now_recognized = face.has_updated_face_id
Return type: bool
-
left_eye
¶ sequence of tuples of float (x,y) – points representing the outline of the left eye.
import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"left_eye: {face.left_eye}")
Return type: List
[CladPoint
]
-
mouth
¶ sequence of tuples of float (x,y) – points representing the outline of the mouth.
import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"mouth: {face.mouth}")
Return type: List
[CladPoint
]
-
name
¶ The name Vector has associated with the face.
This string will be empty if the face is not recognized or enrolled.
import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"Face name: {face.name}")
Return type: str
-
nose
¶ sequence of tuples of float (x,y) – points representing the outline of the nose.
import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"nose: {face.nose}")
Return type: List
[CladPoint
]
-
right_eye
¶ sequence of tuples of float (x,y) – points representing the outline of the right eye.
import anki_vector with anki_vector.Robot(enable_face_detection=True) as robot: for face in robot.world.visible_faces: print(f"right_eye: {face.right_eye}")
Return type: List
[CladPoint
]
-
teardown
()¶ All faces will be torn down by the world when no longer needed.
-
-
class
anki_vector.faces.
FaceComponent
(robot)¶ Manage the state of the faces on the robot.
-
erase_all_enrolled_faces
()¶ Erase the enrollment (name) records for all faces.
import anki_vector with anki_vector.Robot() as robot: robot.faces.erase_all_enrolled_faces()
-
erase_enrolled_face_by_id
(face_id)¶ Erase the enrollment (name) record for the face with this ID.
Parameters: face_id ( int
) – The ID of the face to erase.import anki_vector with anki_vector.Robot() as robot: robot.faces.erase_enrolled_face_by_id(1)
-
request_enrolled_names
()¶ Asks the robot for the list of names attached to faces that it can identify.
import anki_vector with anki_vector.Robot() as robot: name_data_list = robot.faces.request_enrolled_names() print(f"{name_data_list}")
Return type: RequestEnrolledNamesRequest
-
update_enrolled_face_by_id
(face_id, old_name, new_name)¶ Update the name enrolled for a given face.
Parameters: import anki_vector with anki_vector.Robot() as robot: robot.faces.update_enrolled_face_by_id(1, 'Hanns', 'Boris')
-


public class GameState
extends java.util.Observable
| 构造器和说明 |
|---|
GameState(WindowMain win,
java.lang.String path)
constructor method with incoming parameters
|
| 限定符和类型 | 方法和说明 |
|---|---|
void |
addPlayer(Player p1)
To add a player to the player group
|
void |
changed()
Method to specify the state changes
|
Player |
checkWinner()
To check is there a player a winner
|
void |
gameStart(boolean flag)
will start the game setting player 1 as the current player
|
Group |
getAllPlayers()
method to get all the players
|
Country |
getCountry1()
To get a country
|
Country |
getCountry2()
To get a country
|
Country |
getCurrClick()
Method to get the country via the click operation
|
int |
getCurrPhase()
method to get current phase
|
Player |
getCurrPlayer()
method to get the current player.
|
int |
getFirstRound()
method to get current phase
|
WindowMain |
getWindow()
To get window
|
World |
getWorld()
To get world the players are in
|
void |
setAllPlayers(Group allPlayers)
To set all players
|
void |
setCountry1(Country country1)
To set a country
|
void |
setCountry2(Country country2)
To set a country
|
void |
setCurrClick(Country currClick)
Method to set the country via the click operation
|
void |
setCurrPhase(int p)
To set the current game phase
|
void |
setCurrPlayer(Player p1)
To set current player
|
void |
setFirstRound(int p)
To set the first round number
|
void |
setNextPhase()
Method to set the next phase
|
void |
setNextPlayer()
To set the next player after the current player
|
void |
setWorld(World world)
To set a world
|
java.lang.String |
toString()
The override toString function.
|
void |
updateCountryLabels()
According to the selected map, update the country labels
|
public GameState(WindowMain win, java.lang.String path) throws java.lang.Exception
win
- Window frame of the gamepath
- map path on hard driverjava.lang.Exception
public WindowMain getWindow()
public java.lang.String toString()
toString
在类中 java.lang.Object
public void gameStart(boolean flag)
public void updateCountryLabels()
public Country getCurrClick()
public void setCurrClick(Country currClick)
currClick
- the country is needed to be clickedpublic Player getCurrPlayer()
public Group getAllPlayers()
public int getCurrPhase()
public int getFirstRound()
public World getWorld()
public void setCurrPlayer(Player p1)
p1
- the player that will be set as the current player with Player
typepublic void addPlayer(Player p1)
p1
- a player that will be added to the player group with Player
typepublic void setCurrPhase(int p)
p
- the phase that will be set with int typepublic void setFirstRound(int p)
p
- the round that will be set with int typepublic Player checkWinner()
public void setNextPlayer()
public Country getCountry1()
public void setCountry1(Country country1)
country1
- the desired country that will be setpublic Country getCountry2()
public void setCountry2(Country country2)
country2
- the desired country that will be set with Country typepublic void setWorld(World world)
world
- the desired world that will be set with World typepublic void setAllPlayers(Group allPlayers)
allPlayers
- a desired group of players that need to be setpublic void changed()
public void setNextPhase()


This is a web application that takes a user's birthday and calculates the day of the week they were born and then depending on their gender outputs their Akan Name.Akan names are derived from Ghanian culture. Frequently in Ghana, children are given their first name as a 'day name' which corresponds to the day of the week they were born.
Please click on the buttons below to enter information and find out what your Akan name would be.


--- title: Battery Indicator subtitle: Remaining battery time in your menu bar description: Battery Indicator lets you quickly glance the remaining battery time or percentage right in your menu bar. This is especially useful on macOS Sierra 10.12.2 or later where clicking the battery icon no longer shows the remaining time. type: macOS date: 2017-02-18 ---