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280                                <h2 class="page-title">Computer Society</h2>
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296                <h2>About Us</h2>
297                <p>
298                    The NITK-IEEE Computer Society is a conglomeration of students with a passion for Computer Science and Information Technology. This SIG is a great platform for those who want to gain expertise in computer based technology.
299                </p>
300                <p>
301                    In the true spirit of divide and conquer, our community intends to explore and contribute to the rapidly evolving world of computer technologies
302                    by carrying out research and projects in the broad domains of computer science, in the form of 3 research groups :
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318                </p>
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321                <h4>Core Computer Science</h4>
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323                    Algorithms, Cryptography, Databases, Game Theory, Web & Software Development
324                </p>
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346                <p><h4>Projects completed during Academic Years 2019 to 2023 can be found at</h4></p>
347                <p><a href="https://ieee.nitk.ac.in/virtual-expo/compsoc/" class="btn btn-primary btn-outline">Compsoc Virtual Expo</a></p>
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396                
397                    <div id="projects" class="animate-box">
398                        
399                            <div class="row colorlib-bg-white project-card">
400                                <div class="col-md-12">
401                                    <h3>Learning to Communicate in Multi-Agent Systems</h3>
402                                    <div class="desc">
403                                        <p>We are exploring the application of Reinforcement Learning in Multi-Agent systems to learn communication protocols. We are using value-based and policy-based models to learn emergent communication protocols in competitive and collaborative environments. We also plan to study the applicability of these learning methods on real world problems, like communication in a swarm of autonomous vehicles.</p>
404                                        
405                                            <p><a href= https://github.com/IEEE-NITK/Multi-Agent-Reinforcement-Learning class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
406                                        
407                                    </div>
408                                </div>
409                            </div>
410                        
411                            <div class="row colorlib-bg-white project-card">
412                                <div class="col-md-12">
413                                    <h3>Open Answering System</h3>
414                                    <div class="desc">
415                                        <p>This project aims to build a model to  answer open-domain questions by implementing document retrieval and machine comprehension using Wikipedia as the only source of information.  It combines a search component based on bigram hashing and TF-I
415DF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. The project will further be extended to build a closed domain answering system using NCERT textbooks as the only knowledge source.</p>
416                                        
417                                            <p><a href= https://github.com/IEEE-NITK/open-qas class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
418                                        
419                                    </div>
420                                </div>
421                            </div>
422                        
423                            <div class="row colorlib-bg-white project-card">
424                                <div class="col-md-12">
425                                    <h3>CP Judge</h3>
426                                    <div class="desc">
427                                        <p>A functional, robust and secure platform for hosting our own competitive programming contests which should be able to compile and test the submitted code against previously given test cases. Submitted code would be run with restrictions, including time limit, memory limit, and address other security concerns in sandboxed containers. It is a scalable distributed system to support varied load and hence, should be fault tolerant and resilient to prevent cascading failures.</p>
428                                        
429                                            <p><a href= https://github.com/cpjudge class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
430                                        
431                                    </div>
432                                </div>
433                            </div>
434                        
435                            <div class="row colorlib-bg-white project-card">
436                                <div class="col-md-12">
437                                    <h3>ROP Compiler</h3>
438                                    <div class="desc">
439                                        <p>The aim of this project is to write a Compiler which automates the  ROP(Return Oriented Programming) exploit generation. Instead of writing payload by hand, the compiler does it for you. The project is roughly divided into the following parts.<br>1. Collect all ROP Gadgets from the vulnerable software and store it in a database. 2. Convert gadgets into IR, extract their meanings probably by using constraint solvers or with the help of Symbolic Execution. 3. Chain the gadgets to get the exploit function provided.</p>
440                                        
441                                            <p><a href= https://github.com/ROPCompiler class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
442                                        
443                                    </div>
444                                </div>
445                            </div>
446                        
447                            <div class="row colorlib-bg-white project-card">
448                                <div class="col-md-12">
449                                    <h3>Secure Set Intersection using Secure Multi-Party Communication</h3>
450                                    <div class="desc">
451                                        <p>A secure set intersection protocol between sender S and receiver R on respective inputs X and Y s.t. |X|,|Y| ≤ n, allows R to learn X ∩Y while S learns nothing about R’s inputs. We aim to implement and test one such protocol using oblivious transfer.</p>
452                                        
453                                    </div>
454                                </div>
455                            </div>
456                        
457                            <div class="row colorlib-bg-white project-card">
458                                <div class="col-md-12">
459                                    <h3>Quantum Algorithms</h3>
460                                    <div class="desc">
461                                        <p>This project aims to understand the concepts of quantum computation, especially quantum gates and implement few of the popular quantum algorithms - Shor’s algorithm and Grover’s search algorithm. Shor's algorithm finds the prime factors of an integer in polynomial time on a quantum computer. Grover's search algorithm is a quantum algorithm for searching an unsorted database with quadratic speedup over classical algorithm. These algorithms will be implemented in IBM Q Experience.</p>
462                                        
463                                    </div>
464                                </div>
465                            </div>
466                        
467                            <div class="row colorlib-bg-white project-card">
468                                <div class="col-md-12">
469                                    <h3>Neural Voice Cloning</h3>
470                                    <div class="desc">
471                                        <p>This project aims to build an application that will require few audio samples of the user as input and then using the speaker adaptation technique of voice cloning, will be able to read aloud a paragraph in the recorded voice.Speaker adaptation is based on fine-tuning a multi-speaker generative model with a few cloning samples. In terms of naturalness of the speech and its similarity to original speaker, this method can achieve good performance with better naturalness and similarity, even with very few cloning audios.</p>
472                                        
473                                            <p><a href= https://github.com/IEEE-NITK/Neural-Voice-Cloning class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
474                                        
475                                    </div>
476                                </div>
477                            </div>
478                        
479                            <div class="row colorlib-bg-white project-card">
480                                <div class="col-md-12">
481                                    <h3>NLQ to SQL</h3>
482                                    <div class="desc">
483                                        <p>In this project we aim to develop a model which when given a natural language query, will generate the SQL query for the same given the metadata about the database.<br>A lot of approaches have been used to tackle the problem including the traditional sequence to sequence model which is pretty generalised and doesn’t make use of the full structure of the SQL queries. Hence a new approach which uses sequence to set along with column attention was proposed to address the previous problems. We will use deep learning techniques like RNN and specifically LSTM to encode the input questions and build more deep neural networks on top of these encodings to predict the SQL queries.</p>
484                                        
485                                            <p><a href= https://github.com/IEEE-NITK/NLQ_to_SQL class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
486                                        
487                                    </div>
488                                </div>
489                            </div>
490                        
491                            <div class="row colorlib-bg-white project-card">
492                                <div class="col-md-12">
493                                    <h3>Parallelizing Graph Colouring</h3>
494                                    <div class="desc">
495                                        <p>The project aims to explore different ways and approaches to graph colouring in parallel and achieving a good speedup with respect to current heuristics proposed till date, and predict a chromatic number of the graph which has a reasonable relative error.</p>
496                                        
497                                            <p><a href= https://github.com/IEEE-NITK/Parallel-Graph-Colouring-Algorithms class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
498                                        
499                                    </div>
500                                </div>
501                            </div>
502                        
503                    </div>
504                </div>
505                
506            
507
508                
509                
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518                                    <h3>Arcade Game Solver</h3>
519                                    <div class="desc">
520                                        <p>Arcade Game Solver using Deep Reinforcement Learning was about building game agents that use reinfor
520cement learning to play arcade games and hence learn optimal behaviour. It included working with Deep Learning and Convolutional Neural Networks in Python.</p>
521                                        
522                                            <p><a href= https://github.com/IEEE-NITK/Reinforcement-Learning class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
523                                        
524                                    </div>
525                                </div>
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527                        
528                            <div class="row colorlib-bg-white project-card">
529                                <div class="col-md-12">
530                                    <h3>Bike Sharing</h3>
531                                    <div class="desc">
532                                        <p>Bike Sharing project aimed to build a model that will predict the number of cycles used each day. The project used a SVR model. A webapp was also made which showed the prediction for the given input: date and weather conditions.</p>
533                                        
534                                            <p><a href= https://github.com/IEEE-NITK/Ride-Sharing-Bike-Project class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
535                                        
536                                    </div>
537                                </div>
538                            </div>
539                        
540                            <div class="row colorlib-bg-white project-card">
541                                <div class="col-md-12">
542                                    <h3>Blockchain Free Cryptocurrency</h3>
543                                    <div class="desc">
544                                        <p>A Blockchain Free Cryptocurrency developed in the course of the project addresses the problem of centralization of the blockchain based networks currently in use. The cryptocurrency developed makes use of a transaction based hash graph for verification and validation which is a robust solution to the above problem.</p>
545                                        
546                                            <p><a href= https://github.com/IEEE-NITK/Blockchain-free-cryptocurrency class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
547                                        
548                                    </div>
549                                </div>
550                            </div>
551                        
552                            <div class="row colorlib-bg-white project-card">
553                                <div class="col-md-12">
554                                    <h3>Generative Adversarial Networks</h3>
555                                    <div class="desc">
556                                        <p>Generative Adversarial Networks was a project tasked to study and apply the Generative Adversarial Network model to the image generation problem domain. The project comprised of two recent deep generative models – the Star GAN and the Triple GAN. The Star GAN tackles the unpaired image-to-image translation problem on a given label. A live demonstration. was provided where the user fed in an image of his face along with a parameter for the model to translate (hair color, in our case). This was fed in to a trained Star GAN model and the new image was returned. The Triple GAN focused on conditional image generation on labels learned during training in an unsupervised fashion. The model showed promising results on MNIST and CIFAR-10 for both image generation and unsupervised image classification tasks.</p>
557                                        
558                                            <p><a href= https://github.com/IEEE-NITK/Generative-Adversarial-Networks class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
559                                        
560                                    </div>
561                                </div>
562                            </div>
563                        
564                            <div class="row colorlib-bg-white project-card">
565                                <div class="col-md-12">
566                                    <h3>Handwritten Equation Solver</h3>
567                                    <div class="desc">
568                                        <p>Handwritten Equation Solver was an application design to recognise and solve handwritten mathematical equations. The pipeline consisted of a front android application that captures the handwritten math equation which is then sent to a backend server for all c
568omputations and returns the solutions to the equation back to the android app. In the backend server, the image is pre-processed, and a DFS-based novel algorithm is used in the segmentation of the image. The segmented images are then passed through a Convolutional Neural Network (CNN) model for recognition. The model used was trained on a math symbol dataset and had achieved 98.88% cross validation accuracy. The spatial locality of the math symbols in the image is then used in the formation of the final equation string which is solved using the SymPy math engine.</p>
569                                        
570                                            <p><a href= https://github.com/Handwritten-Equation-Solver/ class="btn btn-primary btn-outline with-arrow" target="_blank">View on Github<i class="fa fa-github"></i></a></p>
571                                        
572                                    </div>
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