cyprus

Google Hash Code 2018 Nicosia Cyprus – Call for participation

 in 2018 / Google / Hash Code  tagged 2018 / code / cyprus / google / hash / hash code by

We’ll be hosting a hub at the University of Cyprus for the Online Qualification Round of Hash Code, a team-based programming competition created by Google for university students and industry professionals. The Online Qualification Round takes place on the 1st of March at 19:30 EET and registered teams from Cyprus are invited to participate from our hub, which will take place at the Computer Science Department. Top scoring teams from the Online Qualification Round will then be invited to Google’s Paris office to compete in the Final Round of the competition in April.

If you’re interested in joining our hub, find a team (two to four people) and register at g.co/hashcode. Make sure to select University of Cyprus from the list of hubs in the Judge System.

For more information about this and other hubs in Cyprus (including the twin event in Limassol) visit https://goo.gl/uuRspx

Hash Code 2018 Nicosia Cyprus – Facebook Event

Thanks!

Address:

Rooms: 101, 102, 103
Department of Computer Science,
Pure and Applied Sciences (FST-01)
University of Cyprus
1 University Avenue
2109 Aglantzia, CYPRUS

Date and Time:

1st March 2018
From: 19:30 EET
To: 23:30 EET

Free Amenities Offered

High speed Internet access
Wi-Fi access to the Internet for your mobile devices (personal computers and smart phones)
Lab computers will be available for use by the participants
Food in the form of snacks and beverages will be available outside the labs

Google Hash Code 2018 – Online Qualification Round Schedule

18:30 EET:

  • The hub will open to the public
  • People can view the live stream on the video projector
  • Teams can set themselves up with the help of the volunteers

19:30 EET:

  • Live stream starts

19:45 EET:

  • Task will be made available, competition starts
  • Scoreboard will be displayed on the video projector
  • Participating teams will be confirmed in the Judge System

23:30 EET:

  • End of the competition
  • Announcement of the score for the local teams

00:00 EET:

  • The hub will close

Digital Innovation with APIs

 in Cyprus Computer Society  tagged CCS / cyprus / google / nicosia / presentation / talk by

The CCS is organizing the following Open Seminar on Current International Technology Trends:

Presentation Title: Digital Innovation with APIs
Speaker: Saad Syed, Google
Date/Time: June 28th 2017 , 17:00-18:30 (16:30-17:00 Welcome Coffee & Registration )
Place: European University Cyprus, Room 208, 2nd, East Block

Free Admission – Registration is required
[download id=”3490″]

Open Seminars On Current International Technology Trends Series: Digital Innovation With APIs

APIs are the foundation for many Digital Native applications. Find out how this important capability will allow you to be more agile, build innovative new products/services and leverage the power of collaboration beyond your organization.

Saad Syed is the Regional Lead for Google Edge Southern EMEA. He is responsible for the Apigee API Management solution set in the region. He has worked extensively with many Leading institutions in the region on their Digital Transformation initiatives. Prior to Google Saad was the Regional Leader for Data Center at Cisco Systems for Middle East Turkey Russia and Africa.

Seminar Target Group

  • Innovators
  • Developers
  • Engineers
  • ICT Companies
  • Telecom Companies
  • Banks
  • Insurance Companies
  • Students And Professors

Free Admission – Registration is required
[download id=”3490″]

Cyprus Computer Society – Logos

 in Cyprus Computer Society  tagged CCS / Computer / cyprus / cyprus computer society / high quality / hq / Logos / Society by

The Cyprus Computer Society (CCS) is a professional and independent not-for-profit organization, seeking to improve and promote high standards amongst informatics professionals, in recognition of the impact that informatics has on employment, business, society as well as on the quality of life of the citizen.

Through the advancement of IT Science and good practice our organization promotes wider social and economic progress, bringing together industry, academics, practitioners and government to share knowledge, promote new thinking, inform the design of new curricula, shape public policy and inform the public.

As the Body of Computer Science and IT Professionals, the CCS is engaged in a range of activities, expresses its views on behalf of its members to the National Authorities on IT strategic issues and engages in European and other projects by developing multistakeholder co-operations with the academic community, government, public, private and non-governmental organizations.

The CCS was founded in 1984 and today with the high quality level of the services it offers to its (more than 1200) members, the IT community and the society, the CCS has become the trust-worthy reference point of IT in Cyprus.

Cyprus Computer Society Logo (CCS.org.cy) in Black and White George Michael bytefreaks.net

Cyprus Computer Society Logo (CCS.org.cy) in Color George Michael bytefreaks.net

Online Qualification Round Problem for Google Hash Code 2017

 in 2017 / Google / Hash Code  tagged 2017 / code / cyprus / google / hash / hash code / limassol / nicosia / online qualification / problem by

[download id=”2740″]

[download id=”2739″]

Streaming videos

Problem statement for Online Qualification Round, Hash Code 2017

Introduction

Have you ever wondered what happens behind the scenes when you watch a YouTube video? As more and more people watch online videos (and as the size of these videos increases), it is critical that video-serving infrastructure is optimized to handle requests reliably and quickly.

This typically involves putting in place cache servers, which store copies of popular videos. When a user request for a particular video arrives, it can be handled by a cache server close to the user, rather than by a remote data center thousands of kilometers away.

But how should you decide which videos to put in which cache servers?

Task

Given a description of cache servers, network endpoints and videos, along with predicted requests for individual videos, decide which videos to put in which cache server in order to minimize the average waiting time for all requests.

Problem description

The picture below represents the video serving network.

Videos

Each video has a size given in megabytes (MB). The data center stores ​ all videos​ . Additionally, each video can be put in 0, 1, or more cache servers​. Each cache server has a maximum capacity given in megabytes.

Endpoints

Each endpoint represents a group of users connecting to the Internet in the same geographical area (for example, a neighborhood in a city). Every endpoint is connected to the data center. Additionally, each endpoint may (but doesn’t have to) be connected to 1 or more cache servers​ .

Each endpoint is characterized by the latency of its connection to the data center (how long it takes to serve a video from the data center to a user in this endpoint), and by the latencies to each cache server that the endpoint is connected to (how long it takes to serve a video stored in the given cache server to a user in this endpoint).

Requests

The predicted requests provide data on how many times a particular video is requested from a particular endpoint.

Input data set

The input data is provided as a data set file – a plain text file containing exclusively ASCII characters with a single \n character at the end of each line (UNIX-​ style line endings).

Videos, endpoints and cache servers are referenced by integer IDs. There are V videos numbered from 0 to V − 1 , E endpoints numbered from 0 to E − 1 and C cache servers numbered from 0 to C − 1 .

File format

All numbers mentioned in the specification are natural numbers that fit within the indicated ranges. When multiple numbers appear in a single line, they are separated by a single space.

The first line of the input contains the following numbers:

  • V ( 1 ≤ V ≤ 10000) – the number of videos
  • E ( 1 ≤ E ≤ 1000) – the number of endpoints
  • R ( 1 ≤ R ≤ 1000000) – the number of request descriptions
  • C ( 1 ≤ C ≤ 1000) – the number of cache servers
  • X ( 1 ≤ X ≤ 500000) – the capacity of each cache server in megabytes

The next line contains ​V numbers describing the sizes of individual videos in megabytes: S0, S1, … SV-1. Si is the size of video i​ in megabytes ( 1 ≤ Si ≤ 1000).

The next section describes each of the endpoints one after another, from endpoint 0 to endpoint E − 1 . The description of each endpoint consists of the following lines:

  • a line containing two numbers:
    • LD ( 2 ≤ LD ≤ 4000) – the latency of serving a video request from the data center to this endpoint, in milliseconds
    • K ( 0 ≤ K ≤ C ) – the number of cache servers that this endpoint is connected to
  • K lines describing the connections from the endpoint to each of the K connected cache servers.
    Each line contains the following numbers:

    • c ( 0 ≤ c < C ) – the ID of the cache server
    • Lc ( 1 ≤ Lc ≤ 500) – the latency of serving a video request from this cache server to this endpoint, in milliseconds. You can assume that latency from the cache is strictly lower than latency from the data center ( 1 ≤ Lc < LD

Finally, the last section contains R request descriptions in separate lines. Each line contains the following numbers:

  • Rv ( 0 ≤ Rv < V ) – the ID of the requested video
  • Re ( 0 ≤ Re < E ) – the ID of the endpoint from which the requests are coming from
  • Rn ( 0 < Rn ≤ 10000) – the number of requests

Example

5 2 4 3 100
50 50 80 30 110
1000 3
0 100
2 200
1 300
500 0
3 0 1500
0 1 1000
4 0 500
1 0 1000

Example input file explanation.

5 videos, 2 endpoints, 4 request descriptions, 3 caches 100MB each.
Videos 0, 1, 2, 3, 4 have sizes 50MB, 50MB, 80MB, 30MB, 110MB.
Endpoint 0 has 1000ms datacenter latency and is connected to 3 caches:
The latency (of endpoint 0) to cache 0 is 100ms.
The latency (of endpoint 0) to cache 2 is 200ms.
The latency (of endpoint 0) to cache 1 is 200ms.
Endpoint 1 has 500ms datacenter latency and is not connected to a cache.
1500 requests for video 3 coming from endpoint 0.
1000 requests for video 0 coming from endpoint 1.
500 requests for video 4 coming from endpoint 0.
1000 requests for video 1 coming from endpoint 0.

Connections and latencies between the endpoints and caches of example input.

Submissions

File format

Your submission should start with a line containing a single number N ( 0 ≤ N ≤ C ) – the number of cache server descriptions to follow.

Each of the subsequent N lines should describe the videos cached in a single cache server. It should contain the following numbers:

  • c ( 0 ≤ c < C ) – the ID of the cache server being described,
  • the IDs of the videos stored in this cache server: v0, …, vn ( 0 ≤ vi < V) (at least 0 and at most V numbers), given in any order without repetitions

Each cache server should be described in at most one line. It is not necessary to describe all cache servers: if a cache does not occur in the submission, this cache server will be considered as empty. Cache servers can be described in any order.

Example

3
0 2
1 3 1
2 0 1

Example submission file explanation.

We are using  all 3 cache servers.
Cache server 0 contains only video 2.
Cache server 1 contains videos 3 and 1.
Cache server 2 contains videos 0 and 1.

Validation

The output file is valid if it meets the following criteria:

  • the format matches the description above
  • the total size of videos stored in each cache server does not exceed the maximum cache server capacity

Scoring

The score is the average time saved per request, in microseconds. (Note that the latencies in the input file are given in milliseconds. The score is given in microseconds to provide a better resolution of results.)
For each request description ( Rv, Re, Rn) in the input file, we choose the best way to stream the video Rv to the endpoint Re. We pick the lowest possible latency L = min(LD, L0, … , Lk−1) , where L​D is the latency of serving a video to the endpoint Re from the data center, and L0, … , Lk−1 are latencies of serving a video to the endpoint Re from each cache server that:

  • is connected to the endpoint Re, and
  • contains the video Rv

The time that was saved for each request is LD

As each request description describes Rn requests, the time saved for the entire request description is Rn × ( LD − L ) .

To compute the total score for the data set, we sum the time saved for individual request descriptions in milliseconds, multiply by 1000 and divide it by the total number of requests in all request descriptions, rounding down.

A schematic representation of the example submission file above​ .

In the example​ above, there are three request descriptions for the endpoint 0

  • 1500 requests for video 3, streamed from cache 1 with 300ms of latency, saving 1000ms − 300ms = 700ms per request
  • 500 requests for video 4, streamed from the data center, saving 0ms per request
  • 1000 requests for video 1, streamed from cache 2 with 200ms of latency saving 800ms per request

There is also one request description for the endpoint 1:

  • 1000 requests for video 0, streamed from the data center, saving 0ms per request

The average time saved is:

( 1500x700 + 500x0 + 1000x800 + 1000x0 )/(1500 + 500 + 1000 + 1000)

which equals 462.5ms. Multiplied by 1000, this gives the score of 462 500​.

Note that there are multiple data sets representing separate instances of the problem. The final score for your team will be the sum of your best scores on the individual data sets.

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