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Articles 481 - 510 of 1053
Full-Text Articles in Computer Sciences
Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo
Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo
Master's Projects
There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an …
The Emergence Of Institutional Repositories: A Conceptual Understanding Of Key Issues Through Review Of Literature, O. P. Saini
The Emergence Of Institutional Repositories: A Conceptual Understanding Of Key Issues Through Review Of Literature, O. P. Saini
Library Philosophy and Practice (e-journal)
It is the responsibility of the libraries to keep update its users by incorporating different technologies or tricks among the services offered to users. The libraries are managing diversified collection in both electronic and physical formats including the theses and dissertations awarded by their respective parent institutes in physical form. The academic libraries are directed by the Indian government through a mandate to protect and preserve the theses and dissertation in electronic form and provide access to the public domain. Institutional Repositories (IRs) have the perspective to store any amount of information electronically. Therefore, many of the academic libraries are …
A Survey On Network Security-Related Data Collection Technologies, Huaqing Lin, Zheng Yan, Yu Chen, Lifang Zhang
A Survey On Network Security-Related Data Collection Technologies, Huaqing Lin, Zheng Yan, Yu Chen, Lifang Zhang
Faculty Publications, Information Systems & Technology
Security threats and economic loss caused by network attacks, intrusions, and vulnerabilities have motivated intensive studies on network security. Normally, data collected in a network system can reflect or can be used to detect security threats. We define these data as network security-related data. Studying and analyzing security-related data can help detect network attacks and intrusions, thus making it possible to further measure the security level of the whole network system. Obviously, the first step in detecting network attacks and intrusions is to collect security-related data. However, in the context of big data and 5G, there exist a number of …
Does The Test Work? Evaluating A Web-Based Language Placement Test, Avizia Long, Sun-Young Shin, Kimberly Geeslin, Erik Willis
Does The Test Work? Evaluating A Web-Based Language Placement Test, Avizia Long, Sun-Young Shin, Kimberly Geeslin, Erik Willis
Faculty Publications
In response to the need for examples of test validation from which everyday language programs can benefit, this paper reports on a study that used Bachman’s (2005) assessment use argument (AUA) framework to examine evidence to support claims made about the intended interpretations and uses of scores based on a new web-based Spanish language placement test. The test, which consisted of 100 items distributed across five item types (sound discrimination, grammar, listening comprehension, reading comprehension, and vocabulary), was tested with 2,201 incoming first-year and transfer students at a large, Midwestern public university. Analyses of internal consistency and validity revealed the …
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Faculty Publications
Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the …
Support Vector Machines For Image Spam Analysis, Aneri Chavda, Katerina Potika, Fabio Di Troia, Mark Stamp
Support Vector Machines For Image Spam Analysis, Aneri Chavda, Katerina Potika, Fabio Di Troia, Mark Stamp
Faculty Publications, Computer Science
Email is one of the most common forms of digital communication. Spam is unsolicited bulk email, while image spam consists of spam text embedded inside an image. Image spam is used as a means to evade text-based spam filters, and hence image spam poses a threat to email-based communication. In this research, we analyze image spam detection using support vector machines (SVMs), which we train on a wide variety of image features. We use a linear SVM to quantify the relative importance of the features under consideration. We also develop and analyze a realistic “challenge” dataset that illustrates the limitations …
On The Effectiveness Of Generic Malware Models, Naman Bagga, Fabio Di Troia, Mark Stamp
On The Effectiveness Of Generic Malware Models, Naman Bagga, Fabio Di Troia, Mark Stamp
Faculty Publications, Computer Science
Malware detection based on machine learning typically involves training and testing models for each malware family under consideration. While such an approach can generally achieve good accuracy, it requires many classification steps, resulting in a slow, inefficient, and potentially impractical process. In contrast, classifying samples as malware or benign based on more generic “families” would be far more efficient. However, extracting common features from extremely general malware families will likely result in a model that is too generic to be useful. In this research, we perform controlled experiments to determine the tradeoff between generality and accuracy—over a variety of machine …
Exploratory Data Analysis And Crime Prediction In San Francisco, Isha Pradhan
Exploratory Data Analysis And Crime Prediction In San Francisco, Isha Pradhan
Master's Projects
Crime has been prevalent in our society for a very long time and it continues to be so even today. The San Francisco Police Department has continued to register numerous such crime cases daily and has released this data to the public as a part of the open data initiative. In this paper, Big Data analysis is used on this dataset and a tool that predicts crime in San Francisco is provided. The focus of the project is to perform an in-depth analysis of the major types of crimes that occurred in the city, observe the trend over the years, …
Deep Learning Based Recommendation Systems, Nishanth Reddy Pinnapareddy
Deep Learning Based Recommendation Systems, Nishanth Reddy Pinnapareddy
Master's Projects
The usage of Internet applications, such as social networking and e-commerce is increasing exponentially, which leads to an increased offered content. Recommender systems help users filter out relevant content from a large pool of available content. The recommender systems play a vital role in today’s internet applications. Collaborative Filtering (CF) is one of the popular technique used to design recommendation systems. This technique recommends new content to users based on preferences that the user and similar users have. However, there are some shortcomings to current CF techniques, which affects negatively the performance of the recommendation models. In recent years, deep …
An Analysis Of Operant Conditioning And Its Relationship With Video Game Addiction, Daniel Vu
An Analysis Of Operant Conditioning And Its Relationship With Video Game Addiction, Daniel Vu
ART 108: Introduction to Games Studies
A report published by the Entertainment Software Association revealed that in 2015, 155 million Americans play video games with an average of two gamers in each game-playing household (Entertainment Software Association, “Essential Facts about the Computer and Video Game Industry”). With this massive popularity that has sprung alongside video games, the question must be asked: how are video games affecting today's people? With the current way some video games are structured, the video game rewards players for achieving certain accomplishments. For example, competitive video games reward players who achieve victories by giving them a higher ranking or other games display …
Random Numbers And Gaming, Sinjin Baglin
Random Numbers And Gaming, Sinjin Baglin
ART 108: Introduction to Games Studies
In Counter Strike: Global Offensive spray pattern control becomes a muscle memory to a player after long periods of playing. It’s a design choice that makes the gunplay between players more about instant crosshair placement with the faster player usually winning. This is very different from the gunplay of the current popular shooter Player Unknown’s Battlegrounds. Player Unknown’s Battleground’s spray pattern for the guns are random. So how does this affect the player experience? Well as opposed to Counter Strike: Global Offensive, the design choice makes gunplay between two players more about how a person can adapt faster …
Fighting Game Difficulty, Andrew Hon
Fighting Game Difficulty, Andrew Hon
ART 108: Introduction to Games Studies
Lowering difficulty in games has become a recent trend amongst gaming companies. The goal of this tactic is to provide a more welcoming platform for players that are new to the franchise. However, this trend has been met with criticism amongst more experienced veterans of their respective games. This essay will touch upon different games within the fighting game genre that have lowered their overall difficulty, and the positive/negative effects of it.
Exploring Oculus Rift: A Historical Analysis Of The ‘Virtual Reality’ Paradigm, Chastin Gammage
Exploring Oculus Rift: A Historical Analysis Of The ‘Virtual Reality’ Paradigm, Chastin Gammage
ART 108: Introduction to Games Studies
This paper will first provide background information about Virtual Reality in order to better analyze its development throughout history and into the future. Next, this essay begins an in-depth historical analysis of how virtual reality has developed prior to 1970, a pivotal year in Virtual Reality history, followed by an exploration of how this development paradigm shifted between the 1970's and the turn of the century. The historical analysis of virtual reality is concluded by covering the modern period from 2000-present. Finally, this paper examines the layout of the virtual reality field in respect to he history and innovations presented.
A Framework For Recommendation Of Highly Popular News Lacking Social Feedback, Nuno Moniz, Luís Torgo, Magdalini Eirinaki, Paula Branco
A Framework For Recommendation Of Highly Popular News Lacking Social Feedback, Nuno Moniz, Luís Torgo, Magdalini Eirinaki, Paula Branco
Faculty Publications
Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and …
Aria A11y Analyzer: Helping Integrate Accessibility Into Websites, Jayashree Prabunathan
Aria A11y Analyzer: Helping Integrate Accessibility Into Websites, Jayashree Prabunathan
Master's Projects
Today, nearly 1 in 5 people have a disability that affects their daily life. These varied disabilities can include blindness, low vision or mobility impairments. When interacting with web content, users with such disabilities rely heavily on various assistive technologies, such as screen readers, keyboard, voice recognition software, etc. Here, assistive technologies are software applications or hardware devices that allows users with disabilities to interact with web and software applications. For instance, a screen reader is a software application that navigates through the page and speaks the content to users. Web accessibility is defined as the ability for assistive technology …
Metamorphic Code Generation Using Llvm, Michael Crawford
Metamorphic Code Generation Using Llvm, Michael Crawford
Master's Projects
Each instance of metamorphic software changes its internal structure, but the function remains essentially the same. Such metamorphism has been used primarily by malware writers as a means of evading signature-based detection. However, metamorphism also has potential beneficial uses in fields related to software protection. In this research, we develop a practical framework within the LLVM compiler that automatically generates metamorphic code, where the user has well-defined control over the degree of morphing applied to the code. We analyze the effectiveness of this metamorphic generator based on Hidden Markov Model (HMM) analysis, and discover that HMMs are effective at detection …
Implementation Of Faceted Values In Node.Js., Andrew Kalenda
Implementation Of Faceted Values In Node.Js., Andrew Kalenda
Master's Projects
Information flow analysis is the study of mechanisms by which developers may protect sensitive data within an ecosystem containing untrusted third-party code. Secure multi-execution is one such mechanism that reliably prevents undesirable information flows, but a programmer’s use of secure multi-execution is itself challenging and prone to error. Faceted values have been shown to provide an alternative to secure multi-execution which is, in theory, functionally equivalent. The purpose of this work is to show that the theory holds in practice by implementing usable faceted values in JavaScript via source code transformation. The primary contribution of this project is to provide …
Detecting Encrypted Malware Using Hidden Markov Models, Dhiviya Dhanasekar
Detecting Encrypted Malware Using Hidden Markov Models, Dhiviya Dhanasekar
Master's Projects
Encrypted code is often present in some types of advanced malware, while such code virtually never appears in legitimate applications. Hence, the presence of encrypted code within an executable file could serve as a strong heuristic for detecting malware. In this research, we consider the feasibility of detecting encrypted code using hidden Markov models.
Measuring The Effectiveness Of Generic Malware Models, Naman Bagga
Measuring The Effectiveness Of Generic Malware Models, Naman Bagga
Master's Projects
Malware detection based on machine learning techniques is often treated as a problem specific to a particular malware family. In such cases, detection involves training and testing models for each malware family. This approach can generally achieve high accuracy, but it requires many classification steps, resulting in a slow, inefficient, and impractical process. In contrast, classifying samples as malware or be- nign based on a single model would be far more efficient. However, such an approach is extremely challenging—extracting common features from a variety of malware fam- ilies might result in a model that is too generic to be useful. …
Word Sense Determination From Wikipedia Data Using Neural Networks, Qiao Liu
Word Sense Determination From Wikipedia Data Using Neural Networks, Qiao Liu
Master's Projects
Many words have multiple meanings. For example, “plant” can mean a type of living organism or a factory. Being able to determine the sense of such words is very useful in natural language processing tasks, such as speech synthesis, question answering, and machine translation. For the project described in this report, we used a modular model to classify the sense of words to be disambiguated. This model consisted of two parts: The first part was a neural-network-based language model to compute continuous vector representations of words from data sets created from Wikipedia pages. The second part classified the meaning of …
Multi Language Browser Support, Swapnil Mohan Patil
Multi Language Browser Support, Swapnil Mohan Patil
Master's Projects
Web browsers have become an increasingly appealing platform for application developers. Browsers make it relatively easy to deliver cross-platform applications. Web browsers have become a de facto universal operating system, and JavaScript its instruction set. Unfortunately, executing any other language than JavaScript in web browser is not usually possible. Previous approaches are either non-portable or demand extensive modifications for programs to work in the browser. Translation to JavaScript (JS) is one option but that can be challenging if the language is sufficiently different from JS. Also, debugging translated applications can be difficult. This paper presents how languages like Scheme and …
Virtualized Baseband Units Consolidation In Advanced Lte Networks Using Mobility- And Power-Aware Algorithms, Uladzimir Karneyenka
Virtualized Baseband Units Consolidation In Advanced Lte Networks Using Mobility- And Power-Aware Algorithms, Uladzimir Karneyenka
Master's Projects
Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental …
Bootbandit: A Macos Bootloader Attack, Armen Boursalian
Bootbandit: A Macos Bootloader Attack, Armen Boursalian
Master's Projects
Full disk encryption (FDE) is used to protect a computer system against data theft by physical access. If a laptop or hard disk drive protected with FDE is stolen or lost, the data remains unreadable without the encryption key. To foil this defense, an intruder can gain physical access to a computer system in a so-called “evil maid” attack, install malware in the boot (pre-operating system) environment, and use the malware to intercept the victim’s password. Such an attack relies on the fact that the system is in a vulnerable state before booting into the operating system. In this paper, …
Cache Management And Load Balancing For 5g Cloud Radio Access Networks, Chin Tsai
Cache Management And Load Balancing For 5g Cloud Radio Access Networks, Chin Tsai
Master's Projects
Cloud radio access network (CRAN) has been proposed for 5G mobile networks. The benefit of a CRAN includes better scalability, flexibility, and performance. The paper introduces a cache management algorithm for a baseband unit of CRAN and load balancing algorithms for virtual machines load within the CRAN. The proposed scheme, exponential decay (EXD) with analytical hierarchy process (AHP), increases hit rate and reduces network traffic. The scheme also provides preferential services for users with a higher service level agreement (SLA). Finally, the experiment shows the proposed load balancing algorithm can reduce the virtual machines’ (VM) queue size and wait time.
Cache Management Schemes For User Equipment Contexts In 5th Generation Cloud Radio Access Networks, Gurpreet Kaur
Cache Management Schemes For User Equipment Contexts In 5th Generation Cloud Radio Access Networks, Gurpreet Kaur
Master's Projects
Advances in cellular network technology continue to develop to address increasing demands from the growing number of devices resulting from the Internet of Things, or IoT. IoT has brought forth countless new equipment competing for service on cellular networks. The latest in cellular technology is 5th Generation Cloud Radio Access Networks, or 5G C-RAN, which consists of an architectural design created specifically to meet novel and necessary requirements for better performance, reduced latency of service, and scalability. As part of this design is the inclusion of a virtual cache, there is a necessity for useful cache management schemes and protocols, …
Time-Efficient Hybrid Approach For Facial Expression Recognition, Roshni Velluva Puthanidam
Time-Efficient Hybrid Approach For Facial Expression Recognition, Roshni Velluva Puthanidam
Master's Projects
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of …
“Bluff” With Ai, Tina Philip
“Bluff” With Ai, Tina Philip
Master's Projects
The goal of this project is to build multiple agents for the game Bluff and to conduct experiments as to which performs better. Bluff is a multi-player, non-deterministic card game where players try to get rid of all the cards in their hand. The process of bluffing involves making a move such that it misleads the opponent and thus prove to be of advantage to the player. The strategic complexity in the game arises due to the imperfect or hidden information which means that certain relevant details about the game are unknown to the players. Multiple agents followed different strategies …
A Scrabble Artificial Intelligence Game, Priyatha Joji Abraham
A Scrabble Artificial Intelligence Game, Priyatha Joji Abraham
Master's Projects
Computer AI players have already surpassed human opponents in competitive Scrabble, however, defeating a Computer AI opponent is complex and demands efficient heuristics. The primary objective of this project is to build two intelligent AI players from scr atch for the Scrabble cross - board puzzle game having different move generation heuristics and endgame strategies to evaluate their performance based on various benchmarks like winning criteria, quality of moves, and time consumption. The first AI selected is the most popular Scrabble AI, Maven. It generates a three - ply look - ahead simulation to evaluate the most promising candidate move …
Improve And Implement An Open Source Question Answering System, Salil Shenoy
Improve And Implement An Open Source Question Answering System, Salil Shenoy
Master's Projects
A question answer system takes queries from the user in natural language and returns a short concise answer which best fits the response to the question. This report discusses the integration and implementation of question answer systems for English and Hindi as part of the open source search engine Yioop. We have implemented a question answer system for English and Hindi, keeping in mind users who use these languages as their primary language. The user should be able to query a set of documents and should get the answers in the same language. English and Hindi are very different when …
Question Type Recognition Using Natural Language Input, Aishwarya Soni
Question Type Recognition Using Natural Language Input, Aishwarya Soni
Master's Projects
Recently, numerous specialists are concentrating on the utilization of Natural Language Processing (NLP) systems in various domains, for example, data extraction and content mining. One of the difficulties with these innovations is building up a precise Question and Answering (QA) System. Question type recognition is the most significant task in a QA system, for example, chat bots. Organization such as National Institute of Standards (NIST) hosts a conference series called as Text REtrieval Conference (TREC) series which keeps a competition every year to encourage and improve the technique of information retrieval from a large corpus of text. When a user …