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Articles 601 - 630 of 1053
Full-Text Articles in Computer Sciences
Pro-Fit: Exercise With Friends, Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, Iraklis Varlamis
Pro-Fit: Exercise With Friends, Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, Iraklis Varlamis
Faculty Publications
The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous commitment from the end users, who need to proactively interact with the application in order to connect with friends and attain their goals. These applications fail to engage and motivate users who have busy schedules, or are not as committed and self-motivated. In this work, we present PRO-Fit, a personalized fitness assistant application that employs machine learning and recommendation algorithms in …
Threshold-Bounded Influence Dominating Sets For Recommendations In Social Networks, Magdalini Eirinaki, Nuno Moniz, Katerina Potika
Threshold-Bounded Influence Dominating Sets For Recommendations In Social Networks, Magdalini Eirinaki, Nuno Moniz, Katerina Potika
Faculty Publications
The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading …
Time-Based Ensembles For Prediction Of Rare Events In News Streams, Nuno Moniz, Luís Torgo, Magdalini Eirinaki
Time-Based Ensembles For Prediction Of Rare Events In News Streams, Nuno Moniz, Luís Torgo, Magdalini Eirinaki
Faculty Publications
Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when …
Pattern Discovery In Dna Using Stochastic Automata, Shweta Shweta
Pattern Discovery In Dna Using Stochastic Automata, Shweta Shweta
Master's Projects
We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. …
Cryptanalysis Of The Purple Cipher Using Random Restarts, Aparna Shikhare
Cryptanalysis Of The Purple Cipher Using Random Restarts, Aparna Shikhare
Master's Projects
Cryptanalysis is the process of trying to analyze ciphers, cipher text, and crypto systems, which may exploit any loopholes or weaknesses in the systems, leading us to an understanding of the key used to encrypt the data. This project uses Expectation Maximization (EM) approach using numerous restarts to attack decipherment problems such as the Purple Cipher. In this research, we perform cryptanalysis of the Purple cipher using genetic algorithms and hidden Markov models (HMM). If the Purple cipher has a fixed plugboard, we show that genetic algorithms are successful in retrieving the plaintext from cipher text with high accuracy. On …
Comparative Analysis Of Two Clustering Algorithms: K-Means And Fsdp (Fast Search And Find Of Density Peaks), Li Miao
Master's Projects
With the overwhelming amount of data pouring into our lives, obtaining meaningful information from them is becoming a must task for people. How can people mine for "gold" in this area? Or, what tools can they use to do that? It has been proved that clustering is one of the best tools. In this project, two clustering algorithms are studied and numerically compared with various data sets. The first one is the K-means clustering which starts with initial roughly-guessed clusters, tries to classify some data points into one cluster, and iteratively repeats until converges. The second algorithm is called Fast …
Bitfed, A Centralized Cryptocurrency With Distributed Miners, Shruti Sharma
Bitfed, A Centralized Cryptocurrency With Distributed Miners, Shruti Sharma
Master's Projects
Bitcoin is a decentralized peer-to-peer electronic currency wherein all the payments are sent from one transactor to another directly [1]. Financial institutions are not present in the protocol, hence, there are lower processing fees. The distributed nature provides resilience to Bitcoin transactions, and it operates on mathematical principles and cryptographic proofs. As per Bitcoin generation algorithm, the number of bitcoins in existence will never surpass 21 million, which will lead to deflation and encourage hoarding. In this project, we have implemented a Bitcoin-like currency in order to mitigate the issue of deflation [7]. The idea for our protocol is based …
Sharedwealth: A Cryptocurrency To Reward Miners Evenly, Siddiq Ahmed Syed
Sharedwealth: A Cryptocurrency To Reward Miners Evenly, Siddiq Ahmed Syed
Master's Projects
Bitcoin [19] is a decentralized cryptocurrency that has recently gained popularity and has emerged as a popular medium of exchange. The total market capitalization is around 1.5 billion US dollars as of October 2013 [28]. All the operations of Bitcoin are maintained in a distributed public global ledger known as a block chain which consists of all the successful transactions that have ever taken place. The security of a block chain is maintained by a chain of cryptographic puzzles solved by participants called miners, who in return are rewarded with bitcoins. To be successful, the miner has to put in …
Interactive Phishing Filter, Rushikesh Joshi
Interactive Phishing Filter, Rushikesh Joshi
Master's Projects
Phishing is one of the prevalent techniques used by attackers to breach security and steal private and confidential information. It has compromised millions of users’ data. Blacklisting websites and heuristic-based methods are common approaches to detect a phishing website. The blacklist method suffers from a window of vulnerability. Many heuristics were proposed in the past. Some of them have better accuracy but a lower performance. A phishing filter should have better accuracy and peformance. It should be able to detect fresh phishing websites. Jo et al. [2] present a list of attributes of the web page to find the disparity …
Metamorphic Code Generator Based On Bytecode Of Llvm Ir, Arjun Shah
Metamorphic Code Generator Based On Bytecode Of Llvm Ir, Arjun Shah
Master's Projects
Metamorphic software is famous for changing the internal structure of the code while keeping the functionality same. In order to escape the signature detection along with some advanced detection techniques, many malware writers have used metamorphism as the means. On the other hand, code morphing technique increases the diversity of the software which is considered to be a potential security advantage. In our paper, we have developed a metamorphic code generator based on the LLVM framework. The architecture of LLVM has a three-phase compiler design which includes the front end, the optimizer and the back end. It also gives assistance …
Neural Network Captcha Cracker, Geetika Garg
Neural Network Captcha Cracker, Geetika Garg
Master's Projects
NEURAL NETWORK CAPTCHA CRACKER A CAPTCHA (acronym for "Completely Automated Public Turing test to tell Computers and Humans Apart") is a type of challenge-response test used to determine whether or not a user providing the response is human. In this project, we used a deep neural network framework for CAPTCHA recognition. The core idea of the project is to learn a model that breaks image-based CAPTCHAs. We used convolutional neural networks and recurrent neural networks instead of the conventional methods of CAPTCHA breaking based on segmenting and recognizing a CAPTCHA. Our models consist of two convolutional layers to learn image …
Scalable Techniques For Similarity Search, Siddartha Reddy Nagireddy
Scalable Techniques For Similarity Search, Siddartha Reddy Nagireddy
Master's Projects
Document similarity is similar to the nearest neighbour problem and has applications in various domains. In order to determine the similarity / dissimilarity of the documents first they need to be converted into sets containing shingles. Each document is converted into k-shingles, k being the length of each shingle. The similarity is calculated using Jaccard distance between sets and output into a characteristic matrix, the complexity to parse this matrix is significantly high especially when the sets are large. In this project we explore various approaches such as Min hashing, LSH & Bloom Filter to decrease the matrix size and …
Implementing Type Inference In Jedi, Vaibhav Kamble
Implementing Type Inference In Jedi, Vaibhav Kamble
Master's Projects
This thesis begins with an overview of type systems: evolution, concepts, and problems. This survey is based on type systems of modern languages like Scala and Haskell. Scala has a very sophisticated type system that includes generics, polymorphism, and closures. It has a built-in type inference mechanism that enables the programmer to exclude certain type annotations. It is often not required in Scala to mention the type of a variable because the compiler can infer the type from the initialization of the variable. Study of such type system is demonstrated by the implementation of the type system. A type system …
Pattern-Driven Programming In Scala, Huaxin Pang
Pattern-Driven Programming In Scala, Huaxin Pang
Master's Projects
This is an experimental exploration of the pattern-driven programming paradigm—the sole use of pattern matching to determine the next instruction or execute. We define a pure pattern-driven programming language named PA-Scala by defining a subset of the Scala programming language, which restricts sequence control to the powerful pattern matching facilities in Scala. We use PA-Scala to explore the strengths and limitations of pattern-driven programming. By implementing a phrase structure grammar solver in PA-Scala, we show that pattern-driven programming can be used to solve general computation problems. We then implement a Prolog interpreter in PA-Scala, which demonstrates how resolution and unification …
Pattern-Aided Regression Modelling And Prediction Model Analysis, Naresh Avva
Pattern-Aided Regression Modelling And Prediction Model Analysis, Naresh Avva
Master's Projects
In this research, we develop an application for generating a pattern aided regression (PXR) model, a new type of regression model designed to represent accurate and interpretable prediction model. Our goal is to generate a PXR model using Contrast Pattern Aided Regression (CPXR) method and compare it with the multiple linear regression method. The PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. CPXR is especially effective for high-dimensional data. We use pruning to improve the classification accuracy and to remove outliers from the dataset. We provide implementation details and give …
Question Answering System For Yioop, Niravkumar Patel
Question Answering System For Yioop, Niravkumar Patel
Master's Projects
Yioop is an open source search engine developed and managed by Dr. Christopher Pollett. Currently, Yioop returns the search results of the query in the form of list of URLs, just like other search engines (Google, Bing, DuckDuckGo, etc.) This paper created a new module for Yioop. This new module, known as the Question-Answering (QA) System, takes the search queries in the form of natural language questions and returns results in the form of a short answer that is appropriate to the question asked. This feature is achieved by implementing various functionalities of Natural Language Processing (NLP). By using NLP, …
On-The-Fly Map Generator For Openstreetmap Data Using Webgl, Sreenidhi Pundi Muralidharan
On-The-Fly Map Generator For Openstreetmap Data Using Webgl, Sreenidhi Pundi Muralidharan
Master's Projects
This project describes an approach to create an On-the-fly Map Generator for Openstreetmap Data Using WebGL. The most common methods to generate online maps generate PNG overlay tile images from a wide range of data sources, like GeoJSON, GeoTIFF, PostGIS, CSV, and SQLite, etc., based on the coordinates and zoom-level. This project aims to send vector data for the map to the browser and hence render maps on-the-fly using WebGL. We push all of the vector computation to the GPU. This means that less data needs to be sent to the browser. We have compared existing approaches to our method …
Recommendation System Using Collaborative Filtering, Yunkyoung Lee
Recommendation System Using Collaborative Filtering, Yunkyoung Lee
Master's Projects
Collaborative filtering is one of the well known and most extensive techniques in recommendation system its basic idea is to predict which items a user would be interested in based on their preferences. Recommendation systems using collaborative filtering are able to provide an accurate prediction when enough data is provided, because this technique is based on the user’s preference. User-based collaborative filtering has been very successful in the past to predict the customer’s behavior as the most important part of the recommendation system. However, their widespread use has revealed some real challenges, such as data sparsity and data scalability, with …
A Javascript And Php Epub Reader Web Application, Xiaqing He
A Javascript And Php Epub Reader Web Application, Xiaqing He
Master's Projects
EPUB is one of the most popular ebook formats. It is supported by many e-Readers, such as Apple’s iBooks, BlackBerry Playbooks, Sony Reader, Kobo eReader, Amazon Kindle Fire, and the Mozilla Firefox add-on EPUBReader. In this report, we describe our implementation of a JavaScript and PHP EPUB reader web application. This web application allows users to read the EPUB format books easily across multiple devices without any specified platform lock-in. Our application provides an EPUB library. When a user logs in, he can borrow an EPUB book from the library and save into his own bookshelf, then choose any one …
Predicting 'Attention Deficit Hyperactive Disorder' Using Large Scale Child Data Set, Arpi Shah
Predicting 'Attention Deficit Hyperactive Disorder' Using Large Scale Child Data Set, Arpi Shah
Master's Projects
Attention deficit hyperactivity disorder (ADHD) is a disorder found in children affecting about 9.5% of American children aged 13 years or more. Every year, the number of children diagnosed with ADHD is increasing. There is no single test that can diagnose ADHD. In fact, a health practitioner has to analyze the behavior of the child to determine if the child has ADHD. He has to gather information about the child, and his/her behavior and environment. Because of all these problems in diagnosis, I propose to use Machine Learning techniques to predict ADHD by using large scale child data set. Machine …
Author Recognition Using Locality Sensitive Hashing & Alergia (Stochastic Finite Automata), Prashanth Sandela
Author Recognition Using Locality Sensitive Hashing & Alergia (Stochastic Finite Automata), Prashanth Sandela
Master's Projects
In today’s world data grows very fast. It is difficult to answer questions like 1) Is the content completely written by this author, 2) Did he get few sentences or pages from another author, 3) Is there any way to identify actual author. There are many plagiarism software’s available in the market which identify duplicate content. It doesn’t understand writing pattern involved. There is always a necessity to make an effort to find the original author. Locality sensitive hashing is one such standard for applying hashing to recognize authors writing pattern.
Entity And Relational Queries Over Big Data Storage, Nachappa Achakalera Ponnappa
Entity And Relational Queries Over Big Data Storage, Nachappa Achakalera Ponnappa
Master's Projects
Big data storage involves using NoSQL technologies to handle and process huge volumes of data. NoSQL databases are non-relational, schema-free where data is stored as key-value pairs. The aim of the thesis is to implement Entity and Relational queries on top of Big Data storage. In order to achieve this, we use NoSQL technologies like MongoDB and HBase. We implement various methodologies and solutions on top of MongoDB and HBase to map data across different tables and implement entity and relational queries to retrieve entities from huge volumes of data. We also measure the performance of both the technologies and …
Predicting Autism Over Large-Scale Child Dataset, Arpit Arya
Predicting Autism Over Large-Scale Child Dataset, Arpit Arya
Master's Projects
Data Analytics and Machine learning in healthcare are one of the most emerging and needed fields in current time. Also, a lot of research has been performed and is still being done in this field. In healthcare, gone are those days when only doctor examines and patient listens. Now doctor has a lot of technologies which can assist him and help in accurately diagnosing the disease with which his patient is suffering. The backbone of such technologies is data analytics and machine learning where we can make out a lot of inferences from tons of patients‟ data already available. This …
Load Balancing For Entity Matching Over Big Data Using Sorted Neighborhood, Yogesh Wattamwar
Load Balancing For Entity Matching Over Big Data Using Sorted Neighborhood, Yogesh Wattamwar
Master's Projects
Entity matching also known as entity resolution, duplicate identification, reference reconciliation or record linkage and is a critically important task for data cleaning and data integration. One can think of it, as the task of finding entities matching to the same entity in the real world. These entities can belong to a single source of data, or distributed data-sources. It takes structured data as an input and process includes comparison of that structured data (entity or database record) with entities present in the knowledge base. For large-scale entity, matching data has to go through some sequence of steps, which includes …
Relationship Based Entity Recommendation System, Rakhi Poonam Verma
Relationship Based Entity Recommendation System, Rakhi Poonam Verma
Master's Projects
With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other …
A Secured Cloud System Based On Log Analysis, Sindhusha Doddapaneni
A Secured Cloud System Based On Log Analysis, Sindhusha Doddapaneni
Master's Projects
Now-a-days, enterprises’ acceptance over the Cloud is increasing but businesses are now finding issues related to security. Everyday, users store a large amount of data in the Cloud and user input may be malicious. Therefore, security has become the critical feature in the applications stored in the Cloud. Though there are many existing systems which provide us different encryption algorithms and security methods, there is still a possibility of attacks to applications and increasing data modifications. The idea behind this project is to find attacks and protect the applications stored in the Cloud using log analysis. The proposed solution detects …
Energy Efficiency And Quality Of Services In Virtualized Cloud Radio Access Network, Khushbu Mohta
Energy Efficiency And Quality Of Services In Virtualized Cloud Radio Access Network, Khushbu Mohta
Master's Projects
Cloud Radio Access Network (C-RAN) is being widely studied for soft and green fifth generation of Long Term Evolution - Advanced (LTE-A). The recent technology advancement in network virtualization function (NFV) and software defined radio (SDR) has enabled virtualization of Baseband Units (BBU) and sharing of underlying general purpose processing (GPP) infrastructure. Also, new innovations in optical transport network (OTN) such as Dark Fiber provides low latency and high bandwidth channels that can support C-RAN for more than forty-kilometer radius. All these advancements make C-RAN feasible and practical. Several virtualization strategies and architectures are proposed for C-RAN and it has …
A Completely Covert Audio Channel In Android, Sukanya Thakur
A Completely Covert Audio Channel In Android, Sukanya Thakur
Master's Projects
Exfilteration of private data is a potential security threat against mobile devices. Previous research concerning such threats has generally focused on techniques that are only valid over short distances (NFC, Bluetooth, electromagnetic emanations, and so on). In this research, we develop and analyze an exfilteration attack that has no distance limitation. Specifically, we take advantage of vulnerabilities in Android that enable us to covertly record and exfilterate a voice call. This paper presents a successful implementation of our attack, which records a call (both uplink and downlink voice streams), and inaudibly transmits the recorded voice over a subsequent inaudible call, …
A Recommendation Engine Using Apache Spark, Swapna Kulkarni
A Recommendation Engine Using Apache Spark, Swapna Kulkarni
Master's Projects
The volume of structured and unstructured data has grown at exponential scale in recent days. As a result of this rapid data growth, we are always inundated with plethora of choices in any product or service. It is very natural to get lost in the amazon of such choices and finding hard to make decisions. The project aims at addressing this problem by using entity recommendation. The two main aspects that the project concentrates on are implementing and presenting more accurate entity recommendations to the user and another is dealing with vast amount of data. The project aims at presenting …
Study Of Big Data Arhitecture Lambda Arhitecture, Jaideep Katkar
Study Of Big Data Arhitecture Lambda Arhitecture, Jaideep Katkar
Master's Projects
The lambda architecture introduced by Marz is generic, scalable and fault-tolerant data processing architecture. It aims to satisfy the needs for a robust system that is faulttolerant, both against hardware failures and human mistakes, being able to serve a wide range of workloads and use cases. The architecture proposal decomposes the problem into three layers: a) the batch layer focuses on fault tolerance and optimizes for precise results b) the speed layer is optimized for short response-times and only takes into account the most recent data and c) the serving layer provides low latency views to the results of the …