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How to Increase Memory Power in Students? – Other Money Making Opportunities

Memory is an essential part of building a solid foundation for learning in the classroom and everywhere. Having a good memory can help students do better in school tests, perform well in academics, other co-scholastic activities and achieve better grades in examinations. One of the biggest problems among students is the loss of learn memory & concentration while studying. They tend to forget what they have learnt today in school or tuition.

How to Increase Memory Power in Students?

photo editing – Why does GIMP increase the size of exported JPEG files?

I’m using GIMP to edit some vacation pictures (JPEG files) taken with a rather old digital camera. Lacking a professional background, I just went about by trial and error and ended up adjusting the following parameters:

  • Images are way too dark —> Colors > Levels > Input Levels > Increase Clamp Input, decrease High Input
  • Colors could be more vibrant —> Colors > Auto > Color Enhance

When exporting the edited files, I noticed that the file size has increased by a factor of 3, i.e. files less than 3 MB in size are now 10 MB or larger. What is causing this and is there any way to prevent this without trading away image quality?

google cloud platform – I want to increase the number of files that can be GCP deployed to 10,000 or more

After adding a new library in my local environment and deploying to GCP I got the following error.

$ gcloud app deploy
ERROR: ( INVALID_ARGUMENT: This deployment has too many files. New versions are limited to 10000 files for this app.
- '@type':
  - description: This deployment has too many files. New versions are limited to 10000
      files for this app.
    field: version.deployment.files(...)

I think this error can be avoided by sending a cap increase request, but I’ve never done that so I don’t know what to do.
How can I raise the limit?

dnd 5e – What can a dragon use to increase their armour class?

The barding rules state that any armor can be barding, but they’re unfortunately quite vague on which creatures are proficient with barding — see Are mounts proficient in armour (barding)? for more on this topic. If your DM rules that dragons are proficient with armor, then a dragon can wear a +3 plate armor and have a 21 AC. The Adult Red Dragon has a 19 AC normally, so this helps a bit.

There are no rules that suggest that a monster can wear a shield (“shield-barding”?). We might imagine that a creature would need real arms to wear a shield, and not (eg) forelegs or wings. If your DM nonetheless rules that a dragon can wear a shield, you can factor that into the dragon’s AC.

It’s not clear whether dragons can wear armor while flying. The aaracokra can only wear light armor while flying; the winged tiefling can wear light or medium armor while flying. Your DM will have to issue a ruling on how much armor a dragon can wear while flying.

A ring of protection will give a dragon +1 AC.

Most dragons are not spellcasters, but a dragon might have an ally who is a spellcaster. The spell shield of faith grants +2 AC. The spell haste grants +2 AC as well as other benefits. The spell greater invisibility gives attackers disadvantage. If a dragon is a spellcaster, it can cast one of these spells on itself; dragons have pretty good Constitution saves, so it’s unlikely to fail at concentration.

Most dragons have Frightful Presence, which can also give attackers disadvantage.

unity – How do I increase the spawn rate of an object as the “difficulty” goes on?

I just want my objects to spawn in a faster rate as time goes by. The print(SpawningInBetween); print out shows the percentage of the difficulty overtime.

This is the code I have:

public Vector2 spawnsMinMax;

//Spawn Positions
Vector2 SpawnPosition;
float secondsBetweenSpawn = 1;   
float nextSpawnTime;

void Update()
    if (Time.time > nextSpawnTime)
        float SpawningInBetween = Mathf.Lerp(
        nextSpawnTime = Time.time + SpawningInBetween;

        nextSpawnTime = Time.time + secondsBetweenSpawn;
    // ...

I was thinking of going like this:

if(Time.time > Difficulty.GetDifficultyPercent < 0.9) 

but it got a compiler error.

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How to increase the time that windows 10 use to try to open a file

In my current setup I have some video and photo files on a file server, encrypted, with the use of on demand file access.

If I open these files, some files will not load and give an error that it could not find it, just because the file is not fully downloaded yet. I thought; can I increase the time, and force windows to have another look, instead of comming back with an error? Is it possible? Is it a stupid idea?

for example in vlc log:

      -- logger module started --
main: one instance mode ENABLED
main: Running vlc with the default interface. Use 'cvlc' to use vlc without interface.
dvdnav error: Could not open <file> with libdvdcss.
dvdnav error: Can't open <file> for reading
dvdnav error: vm: failed to open/read the DVD
filesystem error: cannot open file <file> (Permission denied)
      -- logger module stopped --

python 3.x – How can I increase the performances of network flow analysis

I’ve created a short python script in order to do some analysis on network flows.

I have an inventory of networks (/8 to /30 masks) of medium size (around 10k references) that provide several information for each of my networks (physical sites, VRF (routing instances), etc. )

I then have some humongous flows data (millions of flows) that contains precise (/32 masks) source and destination IP.

My main goal is to identify which network my /32 IP belongs to, and then to identify the flows where my destination VRF is the same as my source VRF.

import xlsxwriter
import csv
from import Bar
from ipaddress import IPv4Address, IPv4Network
import operator
import timeit
import os

AnalysedDictionnary = dict()

class Flow:
    def __init__(self, srcIp : IPv4Address, dstIp : IPv4Address, port : int, nbHits : int):
        self.srcIp = srcIp
        self.dstIp = dstIp
        self.port = port
        self.nbHits = nbHits

    def __repr__(self):
        return "<Flow srcIp:%s dstIp:%s port:%s nbHits:%s>" % (self.srcIp, self.dstIp, self.port, self.nbHits)

    def __str__(self):
        return "From str method of Flow: srcIp is %s, dstIp is %s, port is %s, nbHits is %s," % (self.srcIp, self.dstIp, self.port, self.nbHits)

class EntityNetwork:
    def __init__(self, siteId, siteName, vrfId, vrfName, IPV4Networks):
        self.siteId = siteId
        self.siteName = siteName
        self.vrfId = vrfId
        self.vrfName = vrfName
        self.MyIPv4Network = IPV4Networks

class InventoryNetworks:
    # "Reference all current Known Networks"
    def __init__(self, filePath=''):
        self.filePath = filePath
        self.MyEntityNetwork = ()
        # "Read File, parse file and populate a list of IPV4Networks"
        with open(filePath) as csvfile:
            reader = csv.DictReader(csvfile, delimiter=',')
            for line in reader:
                self.MyEntityNetwork.append(EntityNetwork(line('Site ID'), line('Site'), line('VRF (VRF ID)'), line('VRF Description'), IPv4Network(line('Subnet'))))
        self.MyEntityNetwork.sort(key=operator.attrgetter('MyIPv4Network'), reverse=True)

class FlowCapture:
    # An observed flow
    def __init__(self, filePath=''):
        self.filePath = filePath
        self.myFlows = ()
        # "Read File, parse file and populate a list of IPV4Networks"
        file = open(filePath, "r")
        Lines = file.readlines() 
        for line in Lines: 
            splitedLine = line.split(",")
            self.myFlows.append(Flow(IPv4Address(splitedLine(7)), IPv4Address(splitedLine(8)), splitedLine(25), 1))
        tmpMyFlows = set(self.myFlows)
        self.myFlows = list(tmpMyFlows)

class AnalyzedFlow:
    def findSubnet(self, subnet):
        maxMask = 0

        #Check if we already analyzed the network and saved it in the dictionnary
        if subnet in AnalysedDictionnary:
            return AnalysedDictionnary(subnet)

        for flow in self.refNetworks.MyEntityNetwork:
            if flow.MyIPv4Network.overlaps(IPv4Network(IPv4Address(subnet))):
                if flow.MyIPv4Network.prefixlen > maxMask:
                    matchedSubnet = flow
                    maxMask = flow.MyIPv4Network.prefixlen
                    AnalysedDictionnary(subnet) = matchedSubnet
                    #we ordered the subnets by mask size, meaning that we can't find a more precise mask that would match our network, so we can break
        return matchedSubnet

    def __init__(self, flow: Flow, refNetworks : InventoryNetworks):
        self.srcIp = flow.srcIp
        self.dstIp = flow.dstIp
        self.port = flow.port
        self.nbHits = flow.nbHits
        self.refNetworks = refNetworks
        self.srcSubnet = self.findSubnet(self.srcIp)
        self.dstSubnet = self.findSubnet(self.dstIp)

#list of interresting ports
adPort = ('10','12','14','443')

#Create our Inventory of Networks from reference file
script_dir = os.path.dirname(os.path.abspath(__file__))
rel_path = "InventorySheet.csv"
abs_file_path = os.path.join(script_dir, rel_path)
ReferenceNetworks = InventoryNetworks(abs_file_path)

#Create our flows
rel_path = "Logs_Equipment.txt"
abs_file_path = os.path.join(script_dir, rel_path)
CapturedFlows = FlowCapture(abs_file_path)

#Create our analyzed flows
AnalysedFlows = ()
print('its going on')
start_time = timeit.default_timer()

# code you want to evaluate
#(:100) limit to 100 objects in our array so testing time is shorter
bar = Bar('Processing', max=len(CapturedFlows.myFlows(:100)))
for flow in CapturedFlows.myFlows(:100):
elapsed = timeit.default_timer() - start_time
print('we analysed in :', elapsed)

#Filter our analyzed flow
intraVrfFlows = (flow for flow in AnalysedFlows if flow.srcSubnet.vrfId == flow.dstSubnet.vrfId)

adFlows = (flow for flow in AnalysedFlows if flow.port in adPort)

# Create a workbook and add a worksheet.
workbook = xlsxwriter.Workbook('flowAnalysis.xlsx')
worksheetIntraVrfFlows = workbook.add_worksheet('IntraVrfFlows')
worksheetAdFlows = workbook.add_worksheet('AdFlows')

# Start from the first cell. Rows and columns are zero indexed.
def writeSheetIndex(excelSheet):
    excelSheet.write(0,0, 'Source IP')
    excelSheet.write(0,1, 'Source Subnet')
    excelSheet.write(0,2, 'Source VRF')
    excelSheet.write(0,3, 'Source Site')
    excelSheet.write(0,4, 'Port')
    excelSheet.write(0,5, 'Destination IP')
    excelSheet.write(0,6, 'Destination Subnet')
    excelSheet.write(0,7, 'Destination VRF')
    excelSheet.write(0,8, 'Destination Site')

def populateSheetFlow(excelSheet, analyzedFlows):
    nrow = 1
    for flow in analyzedFlows:
        excelSheet.write(nrow,0, str(flow.srcIp))
        excelSheet.write(nrow,1, flow.srcSubnet.MyIPv4Network.with_prefixlen)
        excelSheet.write(nrow,2, flow.srcSubnet.vrfName)
        excelSheet.write(nrow,3, flow.srcSubnet.siteName)
        excelSheet.write(nrow,4, flow.port)
        excelSheet.write(nrow,5, str(flow.dstIp))
        excelSheet.write(nrow,6, flow.dstSubnet.MyIPv4Network.with_prefixlen)
        excelSheet.write(nrow,7, flow.dstSubnet.vrfName)
        excelSheet.write(nrow,8, flow.dstSubnet.siteName)
        if flow.srcSubnet.siteName :
            nrow = nrow + 1

if True :
    populateSheetFlow(worksheetIntraVrfFlows, intraVrfFlows)
    populateSheetFlow(worksheetAdFlows, adFlows)

The first 2 great improvement I’ve found are :

  • Order my networks by mask size so that I can stop searching as soon as I have a match
  • Create a dictionary where I save all my IP//Network match so that I don’t have to iterate on my entire network inventory if I’ve already done the job once for this IP

Is there any other places where I could improve my script performances ?
So far my metrics are about 6 for 100 rows, 30s for 1k row, 120s for 10k row (the more row at once, the more my dictionary is useful).

That’s not too terrible, but I dread how long it will take me to analyse 100 millions of row, so I’d rather find all optimisations I can before.

Thanks for any help.

How to calculate delta Q (modularity increase matrix) in graphs?

I’ve been trying to implement the Three-stage Algorithm to compare its results with our new proposed algorithm with different datasets than those mentioned in the article. I’ve succeeded in implementing the first two stages.

The third stage, however, is what I couldn’t understand. To be precise, I couldn’t understand how the ΔQ matrix is computed. There was no mention of its equation in the article:

A screenshot from the article:

enter image description here

I’ve contacted one of the co-authors of this paper it’s been almost two weeks but they didn’t reply.

Is the third stage the same as the Louvain method?
I’ve found different equations on the internet but they all belonged to the Louvain method.