## Paranoia Play-by-Post GMing Techniques

I am planning on running a play-by-post (pbp) Paranoia Troubleshooters game. Due to the nature of a pbp, its possible that the dramatic tension of a properly run Paranoia game will be lost.

What are ways to increase tension, inter-party distrust, and character information leakage (i.e. players trying to sneak a peak at the "secret" side of the character sheet, or trying to look at other player’s notes to the gm) while retaining the asynchronous aspects of a pbp?

## On the proof techniques of Udi Manber

I was familiar with the approach of first coming up with an algorithm, and then proving the loop invariant to come up with an algorithm as elucidated in CLRS (Introduction to algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein). Lately, on reading Udi Manber’s introduction ‘A creative approach’ I have come across the idea of positing and algorithm and then proving it using induction and also getting the algorithm itself. It’s like having your cake and eating it too.

There is one question which remains inexplicable to me. When I am proving an algorithm using Udi Manber’s approach, am I arguing in the object language or in the meta language? In either of these cases, how am I just generating a proof? From the meta language it seems sensible to generate a proof in the object language, but arguing the soundness/completeness of these class of arguments appears difficult. But how do I guarantee that the proof is correct in the object language if it is generated by the object language itselfa? It is unclear to me if it is the metalanguage or the object language that is involved in here.

This question might seem poorly phrased, but I cannot find a better way to express it. Udi Manber’s approach seems to generate an algorithm despite not knowing a priori what the algorithm itself is. This is something counter intuitive to me. Please kindly explain.

## Is there any rich problems that can be solved using several algorithm techniques?

I am wondering if there are some particularly rich problems that have a large intersection with algorithms and data structures. An example could be the travelling salesman problem. Any other suggestions ?

## Techniques for cursor snap-to-vertex in WebGL or OpenGL?

What’s a technique for snapping the mouse pointer to vertices in WebGL or OpenGL? All I need is the vertex position, no other info.

Ideally, I’d like to do this without needing to keep positions and indices arrays in memory outside of the GPU. BTW, I have already a surface picking technique that uses GPU-resident geometry, which works by sampling the depth buffer at the mouse coordinates and combining that with the unprojected mouse coordinates.

One vague idea: use a vertex colors buffer, that fills triangles with fragments that somehow each encode the position of their nearest vertex position. The fragments could have the absolute position of their nearest vertex, or a vector with length, pointing towards their vertex position. Picking a color on the triangles would provide the nearast vertex position. Though I can’t imagine a way to set up that interpolation on WebGL or OpenGL ES.

Any tips appreciated!

## Algorithm Design Patterns vs. Algorithm Strategies vs. Algorithm Design Techniques vs. Algorithm Design Paradigms

I often encounter these terms, seemingly bearing same semantics and meaning. I’m almost sure they mean same – the types of Algorithms categorized based on their implementation strategy/paradigm.

Just wondering if I’m right and I get this correct, because, again – in different books/courses these terms are encountered in the same context. I’m kind of a very critical to the exactness of the terms I read.

Could you please confirm or reject my assumption?

## what are the parsing techniques of the following languages [closed]

for a report i have to write about parsing techniques of a lot of languages, sadly i cannot find a lot of material about it, so can you help guide me to know how to answer this question?

What type of parsing techniques are used by the following languages?

FORTRAN

Pascal

C/C++

Java (recursive decent parser i think)

XML

Prolog

PHP

Python

Perl

LISP/Scheme

## Is there something like anti-seo techniques? To NOT be found by certain keywords?

I wonder if there is something like anti-seo techniques, enabling me to make a website less findable for certain keywords?

For example: I manage a massage website. This website is purely for massages at home. There are no happy ending or such. It’s purely for professional sports- and relaxing massages.

We also explain this about the services on the website. But as many search for xxx services, we still get weekly requests about it. When googling these type of service, we can also find our massage website on the first page of search results, in between escort services websites.

The part where we talk about not providing such services is inside a `div` with `data-nosnippit` attribute, between `googleoff: index` comments.

``<!--googleoff: index-->   <div data-nosnippet>     Our text about NOT providing these services is in here...   </div> <!--googleon: index> ``

How can we avoid to be in search results for these kind of adult services? Is there something like anti-keywords we can use?

This question (or similar) has probably been asked before, but I could not find it.

## Local Search Techniques (Local Optimality)

I am currently reading about local search techniques. I understand that local search algorithms tend to get stuck in local optima and therefore usually do not find globally optimal solutions. Thus, there are more sophisticated approaches that enable us to leave local optima and may improve the overall solution quality (e.g. simulated annealing or genetic algorithms). What I’m thinking about is the following: As far as I understand it, the simplest approaches like hill-climbing (best-fit) are at least guaranteed to find locally optimal solutions with regard to the specified neighborhood. Don’t we lose this property when using simulated annealing or genetic algorithms? Even for the first-fit version of hill-climbing it is no longer guaranteed to find a local optimum while getting the advantage of a possible runtime reduction. Is it a tradeoff between an increased possibility to reach globally optimal solutions (or a reduced runtime in the first-fit hill-climbing case) and a higher risk of not even getting a locally optimal solution?

## What Are The Off Page Techniques Are Important In 2019?

What Are The Off Page Techniques are Important in 2019?

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