How to create a html file offline with app cache

We have an online html file that is accessible through the browser, we want this to be available offline and looking in to it the best thing would be using the App Cache with a manifest file.

I believe i have done all the steps correctly but it still doesn’t preload all the files for the html. Here is what i have done:

1. Updated the html tag to read

html manifest="location of manifest file"

2. Created a manifest file listing the individual files

CACHE MANIFEST

1360_VT_04data60_VT_03.js

1360_VT_04data60_VT_03.swf

1360_VT_04data60_VT_03.xml

1360_VT_04data60_VT_03_core.xml

etc….

3. Edited the .htaccess file with

AddType text/cache-manifest .manifest ExpiresByType application/x-web-app-manifest+json "access plus 1 year" ExpiresByType text/cache-manifest "access plus 1 year"

Any ideas why this is not working?

Offline bin-packaging problem: probability of a non-optimal solution for the first-fit-decreasing algorithm

For the offline bin packaging problem (non-bounded number of bins, where each bin has a fixed size, and a input with known size that can be sorted beforehand), the first-fit-decreasing algorithm (FFD) gives a solution whose number of bins is, at most, $$\frac{11}{9}\times S_{opt} + \frac{6}{9}$$, or, for the sake of simplification, around $$23\%$$ bigger than the optimal number of bins ($$S_{opt}$$).

Has the probability of getting a non-optimal solution using FFD been ever calculated? Or, in other words, what is the probability of getting a solution whose exact size is $$S_{opt}$$? Or do we have no other choice than assuming that the solution size is evenly distributed in the interval $$[S_{opt}, \frac{11}{9}\times S_{opt} + \frac{6}{9}]$$? Or, as another alternative I can think of right now, is the solution size so dependant on the input that making this question has no sense at all?

And, as a related question, is there any research about what is the NP-hard or NP-complete problem that has an approximation algorithm (of polynomial asymptotic order) with the highest probability of providing an optimal solution?

What is the history of the offline networked mapping tool called Gametable?

Gametable was designed as a networked virtual table top for role playing use. It was comparable to tools such as MapTool, Roll20, Battlegrounds, and others. It’s popularity lay in the utter simplicity of use, virtually no learning curve, and decent if simple tool set.

But after the early 2010’s it fell out of popularity and practically disappeared from search results and reviews.

Thus, what is the history of the offline networked mapping tool called Gametable, as described in the Gametable tutorial link below?

https://www.roleplayingtips.com/articles/gametable_mapping_tutorial.html

Can a Keypass file theoretically be cracked offline?

So you create a .kbdx file, protected by a password.

AFAIK in asymmetric key schemes and in WPA-AES brute-forcing consists of:

• try a random password on the private key / on the router
• if it doesn’t log you in, try another.

So you immediately know did you hit the correct password.

What about a password manager’s database? You know nothing about the content of the file. How do you know you did manage to crack it?

Are Online Problems always harder than the Offline equivalent?

I am currently studying Online-Algorithms, and I just asked myself if online Problems are always harder than the offline equivalent.

The most probable answer ist yes, but I can’t figure the reason out why.

Actually I have a second more specific question. When an offline Problem has some integrality gap ($$IG\in[1,\infty)$$) we know in an offline setting, that there is generally no randomized rounding algorithm which achieves a ratio $$C\geq IG$$.

Can this just be adapted to the online problem? If some fractional algorithm has competitive ratio $$c_{frac}$$ can some randomized rounding scheme only reach competitive ratio as good as $$\frac{c_{frac}}{IG}$$?

What are some good resources out there to help me build an online and offline file converter?

I’m hoping to build both an online and offline file converter (upload a file, convert it to some other specified format, download the new format) as a project. Are there any resources that experienced programmers/hackers/computer scientists recommend? Much appreciated.

Can I use Google Analytics to implement offline conversion tracking?

In a Google Ads account I’m working on, all conversions are imported from Google Analytics. How can I define a Google Analytics goal which has the Google Click ID configurable, i.e. such that reaching the goal is associated with a previously seen Google Click ID? I.e. can I have something to the effect of Offline Conversion Tracking except that I use Google Analytics (and maybe even Google Tag Manager)?

Background:

I’m working on a site which has its analytics managed via Google Tag Manager; some events configured in GTM trigger goals in Google Analytics, which in turn are imported as conversions in Google Ads. For example, “visitor requested a trial account” is a user interaction which is tracked like this.

I’d now like to track if people who requested a trial account actually logged in – and if so, track this as a conversion, too. When a visitor logs into his account, I can check a database to figure out the Google Click ID (if any) which the user got assigned when requesting his account. In case a GCLID is found, I’d like to have a GTM trigger which triggers a tag which bumps a Google Analytics goal (which in turn is imported as a conversion in Google Ads).

Configuring Google Tag Manager accordingly seems straightforward. However, it’s not clear to me what kind of Google Analytics Goal to create which explicitly specifies a click ID.

I recently started learning about randomized online algorithms, and the Wikipedia definitions for the three adversary models are very unhelpful to put it mildly. From poking around I think I have a good understanding of what an oblivious adversary is. From my understanding, the oblivious adversary must determine the “worst possible input sequence” before we even start running our algorithm. Let $$I_w$$ denote the worst possible input sequence this adversary comes up with. (I.e., the input sequence that produces the greatest gap between the best that can be done and what we expect our algorithm to do.)

We then say that our algorithm is $$c$$-competitive (for a minimization problem) under this adversary if $$E[Alg(I_w)] \le c \cdot Opt(I_w) + b$$ where $$c,b$$ are some constants, $$E[Alg(I_w)]$$ is the expected value of our algorithm on the input, and $$Opt(I_w)$$ is the cost if we had made perfect decisions. (I.e., if the problem went offline.)

My confusion concerns the adaptive online and adaptive offline adversaries. I neither fully understand their definitions nor the difference between them. I will list my confusions directly below.

• As I understand it, both of these adversaries somehow build the input sequences as your online algorithm runs. This says before you create the input at time $$t$$, unlike in the case of the oblivious adversary, both the adaptive online and adaptive offline adversaries have access to the outcomes of your algorithm at time steps $$1, \ldots , t-1$$. Then it says that in both cases the adversary “incurs the costs of serving the requests online.” The difference being that for the online adaptive adversary, it “will only receive the decision of the online algorithm after it decided its own response to the request.” Does this mean that the difference is that the offline adaptive adversary can see how your algorithm performs during future steps? Or just the present step? But then why is it still incurring the cost of serving requests online?
• This source contradicts the source above. It says that the adaptive offline adversary “is charged the optimum offline cost for that sequence.” Like I said previously, the previously source says both incur “the cost of serving the requests online.” What does it even mean to incur the cost of serving requests online vs. offline? Which is correct?
• This takes a completely different tack and talks about knowing randomness (online adaptive) vs. knowing “random bits” (offline adaptive). Is this equivalent somehow? How so?
• How does the definition of the competitive ratio change for these two adversaries? Most sources I looked at just defined the competitive ratio for the oblivious adversary.

A simple example of each to illustrate the difference would be much appreciated. Thanks for the help!

MS SQL Express 2016 on Amazon AWS: I Can Take Database Offline but Can’t Bring It Online

I can take databases offline (via GUI) but can’t bring them back online. The server details are as follow:

RDBMS: MS SQL 2016 Express Host: Amazon AWS/RDS Free Tier

Details/History of the Problem A few months ago, I created a db instance on Amazon AWS and at the time of creation, the ‘master/admin’ account was setup via the AWS/RDS web page. With this ‘admin’ account, I have created several databases on that instance without any problems.

Over the past few months, I have used this ‘admin’ account to change several databases to contained databases. I do this so that I can setup contained users. I have also done this several times on this server instance with the same admin account with no problems.

Last night, I had just created a new database via this admin account. I then tried to set this new database as a contained database and the process failed. The dialog box error message stated among other things “please try again later”.

After the 3rd failed attempt, I decided to take the database offline (via the GUI in SSMS). I did this in a bid to force close any possible open processes or connections that might be on this new database. That worked. However, I have not been able to bring it back online. I have tried via the GUI and also via a query and it keeps failing.

I have then checked the server roles assigned to this ‘admin’ account. It is not part of sysadmin role. As I understand, the ‘sysadmin’ role can do absolutely anything on the db instance. I reckon my admin account is not of this sysadmin role because it is meant for the in-house DBAs at Amazon AWS. I have tried to add it as sysadmin but it fails.

To ensure that my ‘admin’ account is the problem, I have taken another database offline (it’s empty). It went offline but it is also failing to come back online.

What could be the problem? Please help. Note that my skill level is very very low and I’m learning as I go along.

The server logs don’t show anything useful. I have attached screenshots.