GOOGLE HASH CODE International Coding Competition

Feb 20th, 2020 , hosted at AU College of Engineering (A), Vizag Hash Code Hub

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 About the event

Andhra University Tech Hub is Google approved centre for organizing the coding competition, Google Hash Code 2020 for the local community of Visakhapatnam.

Time and Venue

The competition took place in Andhra University College of Engineering (A) premises, from 11 P.M. of 20th February, 2020 to 4:00 A.M. of 21st February, 2020.

About Hash Code

Hash Code is a Google’s team-based programming competition, Hash Code, designed to share skills and connect with other coders by working together to solve a problem modelled of a real Google engineering challenge in locally coordinated Hash Code hubs.

Google has been organizing a competition for programmers since 2016 called Hash Code. Every year it attracts thousands of people from all over the world. The top teams from this round are invited to join at an international Google office for an annual Hash Code Final Round. The team should contain at least 2-4 members.

It has two rounds: main and extended. The main round continues only for four hours, while the extended one can go on for up to two weeks. The extended round does not count on the scoreboard, but it’s still a good chance to check one’s knowledge and skills.

TECH-HUB Community

In this approved centre, TECH-HUB COMMUNITY hosted the competition.

TECH-HUB COMMUNITY is an institution-level community of Andhra University College of Engineering(A), constituted with the directions and vision of the Principal of AU College of Engineering (A), Prof P. Srinivasa Rao. The community focuses on providing a platform for the engineering students to help themselves in developing skills as per industry future demands. The main objective of this community is to ‘collaborate, think, build and move together’ to keep up with the pace of changes happening in the Global Technology space.

Participants statistics

There were 494 contestants and 139 teams participated from in and around the Visakhapatnam. Out of them there were 319 boys and 175 girl participants from 12 colleges.

Event Proceedings

The event started with pep talks of our esteemed Principal – Prof. P. Srinivas Rao, Mentor and Coordinator of the event, Prof. V. Valli Kumari, Co-Coordinator, DR K.V Ramana and other eminent guests in the E-class room. All the participants were put up in E-class room, PSN Raju Lab of principal block and Software labs of CSSE department with high-speed Wi-Fi and LAN facility and Systems.

At sharp 11 PM, there was a Livestream from GOOGLE in which the task was announced. After that, the problem statement was released in the Judge system. There started a competitive scenario and excitement among all. Live scoreboards were displayed from around the world until the end of the competition. It encouraged the participants to compete around the world. Throughout the event, we had the support and guidance of our mentor and lab faculty. Special security measures are taken to all who were present.

Refreshments were provided to rejoice their levels. The area was decorated with Google props which had a special attraction and photographs.

Sponsorship

The event was fully sponsored by TEQIP III, AU College of Engineering(A).

Team

 The following are the team members

  1. Prof P. Srinivasa Rao, Patron
  2. Prof V. Valli Kumari, Co-ordinator
  3. Dr K. Venkata Ramana, Co-Coordinator
  4. M. Varun, Student Member
  5. P. Srinivasa Bhargav, Student Member
  6. G. S. Sai Ganesh, Student Member
  7. P. Veda Upasan, Student Member

The conclusion

Finally, all the participants qualified for the online qualification round and received online certificates from Google and Andhra University Tech-hub. All the event was filled with fun, risk, enjoyments. It became a successful event with all the support of participants, administration, mentors, parents.

Attachments

Poster
Participants at entrance at 9 pm
Organisers verifying the participants at the entrance.
Participants during the inauguration event at E-class room.
Honourable Principal delivering his speech at Inaugural event , as Co-ordinator Prof Valli Kumari looks on.
Participants at the inaugural event
Hash Code decorative
Participants at P.S.N.Raju Lab prior to the competition.
Participants in Software Lab-1 at CSSE department.
Participants during the competition in Software Lab-2 at CSSE department.
Participant trying to solve the problem statement.
The organising team
The team and the volunteers

Privacy Inclined Sequential Publishing of dynamic Social Network data

Jyothi Vadisala, Awarded

Social networks are being applied to a wide variety of applications. Social networks model the social activities between individuals, that change as time goes by. Data privacy is a major problem that has to be considered for dynamic networks before releasing network data to the public or third parties like researchers and analyzers that would compute statistics or make a deep analysis of these data. As the demand for dynamic social network analysis increases, the privacy issue in sequential publishing of social network needs to be considered. In this context, many different anonymization techniques have been proposed in the literature.

This thesis aims to address the privacy risks in dynamic social networks and to contribute a few techniques for data anonymization while optimising the privacy- utility tradeoff. Specifically, the proposed models deal with emerging privacy issues in sequential publishing of social network data. These type of data are derived from activities of our everyday life, thus the privacy of such data is impor- tant. The main objective of this thesis work is divided into two research problems.

First research problem, addresses the privacy risks of identity disclosures in sequential releases of a dynamic network. The aim is to propose a privacy model that provides adequate privacy protection and retains enough graph data utility in social network data publication. Specifically, this proposed kw-NMF anonymity model, protects identity disclosure in the context of number of common friends as a background knowledge of an adversary. To achieve this kw-NMF anonymity Number of Mutual Friend Anonymization (NMFA) greedy algorithm is proposed for large scale graph data by adding edges in anonymization process.

Performance evaluation is done on both real and synthetic data sets. The results show that the dynamic kw-NMF anonymity model can retain much of the characteristics of the network while confirming the privacy protection. The metrics used for evaluation are Graph Modification, Average Shortest Path Lengths and Average Clustering Coefficients from the aspects of protections against different information hysteresis (w) and different protection level requirements (k). Experimental results shows that the proposed algorithm can retain much of the characteristics of a dynamic network while confirming the privacy protection.

Second research problem, focuses on how the anonymization methods preserves in the existing communities of the original social networks. The k-degree and k-NMF anonmyization methods are used for anonymizing original social network and de- tect the communities for both original and anymomized networks by heuristic algorithm modular optimization Louvain method. The preservation of community is measured by Percentage of Naive Community Preservation (%NCP) and Percentage of Community Preservation at Node Level (%CPNL).

The performances of the two anonymized social networks are compared with the original social network. The experimental results show that the k-NMF anonymization method preserves the most of the communities of the original social network than k-degree anonymization method.

Feature Selection and Extraction for Inverse Synthetic Aperture Radar Images for Ship Detection and Classification

B. Mamatha

Ocean surveillance is becoming significant since the military coastal activities are becoming more and more frequent, the ship types are increasing fast and the unconventional activities happen often. The traditional sea surveillance methods of monitoring the sea targets only by the metric information such as range, aspect, speed and direction of the target is do not giving proper results for identification of a ship. So, there is a requirement to develop automatic ship recognition systems. A few such recognition systems with ship detection software already exist. Now the challenge is to quantify detection capability and to design new software systems to better detect ship signatures to classify the ship targets of interest. In this work an Feature selection and extraction of Inverse Synthetic Aperture Radar (ISAR) image for ship classification is taken as a research problem. Ship ISAR image is a high resolution radar image.  Radar is a popular sensor for it can be used to acquire target signatures in all conditions and also from hundreds of kilometers of distance. The primary objective is to acquire target characteristics or features from the target signatures acquired through radar. Then these features are used for recognition and classification of targets. ISAR images are radar images acquired through high resolution radar which are widely being used for recognition of sea, air, land and even human targets since they contain useful information of target geometry.

In this work a set of ship ISAR images measured with high resolution radar are taken as a data set. The sea clutter and noise from nearby terrain causes hindrance to obtain clear ship ISAR images. Hence preprocessing of ISAR images is the first step and extraction of ship features having highly discriminative nature and low dimensionality from noise free ISAR images is the next step in ship classification. The problem of classification can be solved by using neural networks. Different feature extraction techniques are applied to the preprocessed ISAR images from the data sets.  The features computed from training data set are used to train the classifier and the features obtained from test set are utilized to quantify the classification accuracy of the feature vector. In this work probabilistic neural network is used as a classifier. In this work study is carried out to identify feature vectors that classify the unique set of ship ISAR images considered in this work. Various statistical techniques and mathematical transforms are used to derive features from ship ISAR images. The statistical descriptors and Zernike moments are found to give good classification accuracy. 

ISAR images are wave decomposed up to five levels with the application of multi resolution wavelet transform. For each wave, decomposed level Image, all approximation and detailed wave coefficients are computed and taken as feature vectors to identify the best suitable wave coefficients for the unique set of ship ISAR images considered in this work. Segmentation is used to find regions of interest in case of medical images, border or shape information of ISAR images or to identify linked homogeneous regions in high resolution remote sensing image analysis applications. Hence keeping in view of the significant role of segmentation in image analysis, above mentioned wave decomposed image features are also studied to find the classification accuracy of the ISAR images with segmentation.

Ship ISAR images are color images. The moments computed from individual color component images R,G,B and combined color component images  RG,GB,RB of the ISAR images taken together are considered as feature vector. Using the multi resolution wavelet transform, the ISAR images are decomposed up to five levels. The average wave energy values are computed for each of the wave decomposed images. Obtained wave energy values  are found to form a good feature vector for the ship ISAR image classification. 

In classification problems, the feature vector plays a significant role since the computational complexity and accuracy of classification depend on the features that constitute a feature vector and also the number of features in the feature vector.  All the features in a feature vector under consideration may not equally contribute to the classification of the images. Some of them may be redundant. Since a feature vector of smaller size with high discriminating nature is always preferred. Hence there is always a need to select an optimum feature set to solve the classification problem at hand. Several available optimization techniques can be explored to select optimum feature set. In this research work, ship ISAR image classification problem is studied as a single objective optimization problem and the different optimum feature combinations that give the satisfactory classification for the considered data set are identified. The optimization techniques like genetic algorithm and particle swarm optimization techniques are employed to identify the optimum feature sets in case of color moments and wave energy levels.

Generic Distributed Framework for cloud services market place based on unified ontology

Samer Hasan, January 2019 Awarded

Cloud computing is a paradigm for delivering ubiquitous and on demand resources based on pay as you use financial model. It turns the IT services into utility like: water, electricity, gas and telephony. Cloud service discovery and selection becomes a significant challenge because of exponential growth in the number of cloud service providers. Typically, Cloud service providers publish cloud service advertisements and Service Level Agreement (SLA) details in various formats on the Internet.  Consumers should explore cloud service provider websites using the existing search engines like (Google and Yahoo) to collect information about all available services and select the best one manually. Unfortunately general purpose search engines are not designed to provide a complete and small set of results that meet the consumer requirements which makes the discovery and selection process a tedious task.

This thesis presents a generic-distrusted framework for cloud services marketplace to: i) automate cloud service discovery and selection process, ii) reduce the time and effort of finding cloud services, iii) make service providers more visible to all consumers, iv) create a shared understanding of cloud service domain and v) improve the overall user experience. In addition, this thesis presents a novel algorithm for cloud services numerical similarity named Percent Distance Similarity (PDS) which is independent of any external values. To overcome the interoperability and vendor lock-in problems, this thesis presents unified cloud services ontology and models the real-life services according to proposed ontology. Finally, this thesis implements two instances of generic framework by adopting two different matching algorithms. First one is based on dominant and recessive attributes algorithm borrowed from gene science and the second one is based on a semantic similarity algorithm and unified cloud service ontology.This thesis is the first attempt to build a cloud service marketplace where cloud service providers and cloud service consumers can trade a cloud service as a utility. It is a global digital market where cloud consumer can find the solution that match his/her needs by simply submitting a request. Comparison done with existing systems on real-life cloud services collected from providers websites based on four parameters (number of matched services, execution time, average Score and recall). Semantic approach based on cloud ontology reduced the execution time by 20% and maintain the same values for all other parameters. On the other hand, Non-semantic approach reduced the execution time by 57% but showed lower value for recall.

Intelligent Evolutionary Approaches based Query Optimisation Algorithms in Distributed Database Systems

S. V Lakshmi , January 2019, PhD Awarded

The thesis work focused on minimizing the tradeoffs that exists
amongst the accuracy, minimum response time and cost of query optimisation techniques using genetic algorithms.
The performance metrics used for the proposed techniques in the thesis are the Average Cost of Query Execution Plan such as Join processing cost, local processing cost and global communication cost based on the accuracies of the executed queries results.

Youth and the culture of Entrepreneurship

We have organised a seminar on ‘ Youth and culture of Entrepreneurship” with special focus on women. with support from IETE Vizag Chapter. Ms Lakshmi Potluri , CEO, DCF Ventures has given a talk. Her motivating session focused on startups essentials like: Idea, Team, Timing and Funds. Around 300 students and teachers participated in the seminar. Prof Srinivasa Rao, Principal , AUCE , Prof Koti Reddy, Chairman , IETE and prof Rajesh, Secretary , IETE spoke on the occasion.

The students interacted with Ms Lakshmi and said they were excited to see co-founder of Jabong!

CTO of FTI visits AU

Joe Boerio is an SVP & CTO at Franklin Templeton Investments, Dublin, California. Mr Joe visited our campus along with his team in India. An interesting discussion on possible collaboration with institutions was done. Mr Joe came out with interesting areas of skills expected from campus graduates. He discussed various topics related to Cyber Security, Privacy, threat intelligence, data analytics.

Safety Measures while making online payments

Just gave a general talk on digital payments and safety measures. Thought would put them here…..

Safety Measures to be taken while using Credit/debit card

Identity theft

Some one can spoof your identity and gain access over your account. He might have known your password or your personal information through which he could reset your password.

Credit card / debit card /online banking

Some one must have got your PIN number, password in addition to your card number.. A few cards are also cloned. Phishing is one popular attack. An SMS or email or whatsapp message is sent with a link placed in it. That link when clicked would take you to web site where you are forced to feed information. Sometimes malware gets inserted in your computer or mobile through which important information can be stolen or deleted. Sometimes phone calls are made asking for information like credit card number, PIN, passwords etc on the pretext that you have won an award or got a lucky draw. This is called Vishing. Skimming is another technique through which credit card numbers are acquired either through copies of receipts or through small electronic devices. Never fill forms of lucky draw in malls.

Sometimes people come to you offering help in feeding information in ATM counters. Do not believe them. See that there are no strange devices connected to the ATM machine.

Keep changing your PIN frequently, do not expose last three digits of your card. Register your phone number and keep checking your transactions.

When a vendor swipes your card, keep an eye on him, an be present at the machine when transaction happens.

Use https web site for transactions and see the stamp that the web site is trusted.

Report immediately if you lost the card. Never throw the card after you get a new card. You tear it of into pieces. When you do online banking, don’t forget to log off.

Do not use unknown computers and free wifi to access your online account.