Computer Sciences Commons^™

Algorithms For Modular Self-Reconfigurable Robots: Decision Making, Planning, And Learning, Ayan Dutta

Student Work

Modular self-reconfigurable robots (MSRs) are composed of multiple robotic modules which can change their connections with each other to take different shapes, commonly known as configurations. Forming different configurations helps the MSR to accomplish different types of tasks in different environments. In this dissertation, we study three different problems in MSRs: partitioning of modules, configuration formation planning and locomotion learning, and we propose algorithmic solutions to solve these problems.

Partitioning of modules is a decision-making problem for MSRs where each module decides which partition or team of modules it should be in. To find the best set of partitions is …

Towards Student Engagement Analytics: Applying Machine Learning To Student Posts In Online Lecture Videos, Nicholas R. Stepanek

Student Work

The use of online learning environments in higher education is becoming ever more prevalent with the inception of MOOCs (Massive Open Online Courses) and the increase in online and flipped courses at universities. Although the online systems used to deliver course content make education more accessible, students often express frustration with the lack of assistance during online lecture videos. Instructors express concern that students are not engaging with the course material in online environments, and rely on affordances within these systems to figure out what students are doing. With many online learning environments storing log data about students usage of …

Encrypted Data Processing With Homomorphic Re-Encryption, Wenxiu Ding, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud computing offers various services to users by re-arranging storage and computing resources. In order to preserve data privacy, cloud users may choose to upload encrypted data rather than raw data to the cloud. However, processing and analyzing encrypted data are challenging problems, which have received increasing attention in recent years. Homomorphic Encryption (HE) was proposed to support computation on encrypted data and ensure data confidentiality simultaneously. However, a limitation of HE is it is a single user system, which means it only allows the party that owns a homomorphic decryption key to decrypt processed ciphertexts. Original HE cannot support …

Parametric Model Checking Timed Automata Under Non-Zenoness Assumption, Étienne Andre, Hoang Gia Nguyen, Laure Petrucci, Jun Sun

Research Collection School Of Computing and Information Systems

Real-time systems often involve hard timing constraints and concurrency, and are notoriously hard to design or verify. Given a model of a real-time system and a property, parametric model-checking aims at synthesizing timing valuations such that the model satisfies the property. However, the counter-example returned by such a procedure may be Zeno (an infinite number of discrete actions occurring in a finite time), which is unrealistic. We show here that synthesizing parameter valuations such that at least one counterexample run is non-Zeno is undecidable for parametric timed automata (PTAs). Still, we propose a semi-algorithm based on a transformation of PTAs …

Stop Nuclear Smuggling Through Efficient Container Inspection, Xinrun Wang, Qingyu Guo, Bo An

Research Collection School Of Computing and Information Systems

Since 2003, the U.S. government has spent $850 million on the Megaport Initiative which aims at stopping the nuclear smuggling in international container shipping through advanced inspection facilities including Non-Intrusive Inspection (NII) and Mobile Radiation Detection and Identification System (MRDIS). Unfortunately, it remains a significant challenge to efficiently inspect more than 11.7 million containers imported to the U.S. due to the limited inspection resources. Moreover, existing work in container inspection neglects the sophisticated behavior of the smuggler who can surveil the inspector’s strategy and decide the optimal (sequential) smuggling plan. This paper is the first to tackle this challenging container …

Impact Of Artificial Intelligence, Robotics, And Machine Learning On Sales And Marketing, Keng Siau, Y. Yang

Research Collection School Of Computing and Information Systems

AI, robotics, and machine learning are impacting the field of sales and marketing in an unprecedented way. A perfect storm is brewing! On one hand, online retail stores like Amazon are crushing the bricks and mortar stores. Sales and marketing professionals in bricks and mortar stores are facing a grim future. On the other hand, AI, robotics, and machine learning are replacing sales and marketing professionals in online stores. In fact, salespersons and marketers are predicted to be among the first to be replaced by robots. In a face-to-face environment, human may still prefer to interact with another human. In …

Machine Learning Approaches To Sentiment Analytics, W. Zhao, Keng Siau

Research Collection School Of Computing and Information Systems

One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces …

Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we …

Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun

Research Collection School Of Computing and Information Systems

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for the CTR model has some critical limitations: First of all, it is designed to work in a batch learning manner, making it unsuitable to deal with streaming data or big data in real-world recommender systems. Secondly, in the existing algorithm, the item-specific topic proportions of LDA are fed to the downstream PMF but the rating information is not exploited in discovering …

Online/Offline Provable Data Possession, Yujue Wang, Qianhong Wu, Bo Qin, Shaohua Tang, Willy Susilo

Research Collection School Of Computing and Information Systems

Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires …

Follow-My-Lead: Intuitive Indoor Path Creation And Navigation Using See-Through Interactive Videos, Quentin Roy, Simon T. Perrault, Shengdong Zhao, Richard Davis, Anuroop Pattena Vaniyar, Velko Vechev, Youngki Lee, Archan Misra

Research Collection School Of Computing and Information Systems

We present Follow-My-Lead, an alternative indoor navigation technique that uses visual information recorded on an actual navigation path as a navigational guide. Its design revealed a trade-off between the fidelity of information provided to users and their effort to acquire it. Our first experiment revealed that scrolling through a continuous image stream of the navigation path is highly informative, but it becomes tedious with constant use. Discrete image checkpoints require less effort, but can be confusing. A balance may be struck by adding fast video transitions between image checkpoints, but precise control is required to handle difficult situations. Authoring still …

Exploiting Anonymity And Homogeneity In Factored Dec-Mdps Through Pre-Computed Binomial Distributions, Rajiv Ranjan Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Recent work in decentralized stochastic planning for cooperative agents has focussed on exploiting omogeneity of agents and anonymity in interactions to solve problems with large numbers of agents. Due to a linear optimization formulation that computes joint policy and an objective that indirectly approximates joint expected reward with reward for expected number of agents in all state, action pairs, these approaches have ensured improved scalability. Such an objective closely approximates joint expected reward when there are many agents, due to law of large numbers. However, the performance deteriorates in problems with fewer agents. In this paper, we improve on the …

Data-Driven Approach To Measuring The Level Of Press Freedom Using Media Attention Diversity From Unfiltered News, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Published by Reporters Without Borders every year, the Press Freedom Index (PFI) reflects the fear and tension in the newsroom pushed by the government and private sectors. While the PFI is invaluable in monitoring media environ- ments worldwide, the current survey-based method has in- herent limitations to updates in terms of cost and time. In this work, we introduce an alternative way to measure the level of press freedom using media attention diversity compiled from Unfiltered News.

Fusing Social Media And Mobile Analytics For Urban Sense-Making, Archan Misra

Research Collection School Of Computing and Information Systems

The project was motivated by the observation that urban environments are increasingly characterized by a variety of non-traditional “sensors”, whose data streams can be harnessed to infer a variety of latent events and urban context. For example, users spontaneously generate huge amounts of content (text, images and video) on social network channels, whereas GPS & other sensors on taxis and buses increasingly provide near-real time traces of their movement throughout the city. Similarly, advances in Wi-Fi based sensing allow us to passively capture the individual and collective movement of visitors across various public spaces, such as college campuses, museums and …

An Investigation Into The Performance Evaluation Of Connected Vehicle Applications: From Real-World Experiment To Parallel Simulation Paradigm, Md Salman Ahmed

Electronic Theses and Dissertations

A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than …

Glhf: A Brief Overview Of Gaming Cafes, John Sun

ART 108: Introduction to Games Studies

My paper is on the history of internet and gaming cafes, focusing on how they are seen today, the problems they face and some potential solutions. Although my focus is on gaming cafes in America, because they are so popular overseas in Asian countries (such as South Korea, China, and Japan), they inevitably come up more often in my paper and presentation.

Dpweka: Achieving Differential Privacy In Weka, Srinidhi Katla

Graduate Theses and Dissertations

Organizations belonging to the government, commercial, and non-profit industries collect and store large amounts of sensitive data, which include medical, financial, and personal information. They use data mining methods to formulate business strategies that yield high long-term and short-term financial benefits. While analyzing such data, the private information of the individuals present in the data must be protected for moral and legal reasons. Current practices such as redacting sensitive attributes, releasing only the aggregate values, and query auditing do not provide sufficient protection against an adversary armed with auxiliary information. In the presence of additional background information, the privacy protection …

Open Census For Addressing False Identity Attacks In Decentralized Social Networks, Song Qin

Theses and Dissertations

We address the problem of estimating the population of a given area (e.g., a city). Unlike a centralized census where identities are reported to a single principal agency (e.g., U.S. Census Bureau) and are stored on a centralized data server, the proposed decentralized census distributes reported identities in a peer-to-peer (P2P) network. Peers in this network can also distribute opinions on whether some of the reported identities are correct or false. The estimation of the count of correct identities is based on the available information. A decentralized census enables individuals to independently verify governmental census data. The results of a …

Bird's Eye View: Cooperative Exploration By Ugv And Uav, Shannon Hood

Theses and Dissertations

This paper proposes a solution to the problem of cooperative exploration using an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). More specifically, the UGV navigates through the free space, and the UAV provides enhanced situational awareness via its higher vantage point. The motivating application is search and rescue in a damaged building. A camera atop the UGV is used to track a fiducial tag on the underside of the UAV, allowing the UAV to maintain a fixed pose relative to the UGV. Furthermore, the UAV uses its front facing camera to provide a birds-eye-view to the remote …

Binary Analysis Framework, Josh Stroschein

Masters Theses & Doctoral Dissertations

The binary analysis of software has become an integral activity for security researchers and attackers alike. As the value of being able to exploit a vulnerability has increased, the need to discover, fix and prevent such vulnerabilities has never been greater. This paper proposes the Binary Analysis Framework, which is intended to be used by security researchers to query and analyze information about system and third party libraries. Researchers can use the tool to evaluate and discover unknown vulnerabilities in these libraries. Furthermore, the framework can be utilized to analyze mitigation techniques implemented by operating system and thirdparty vendors. The …

Object Recognition In Videos Utilizing Hierarchical And Temporal Objectness With Deep Neural Networks, Liang Peng

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

As the growth of mobile devices and social networks has been faster than ever, online image and video content has become truly ubiquitous today. Understanding of these images and videos, called vision, is one of the most primary ways for human being to perceive the world. Computer vision, which refers to the study of enabling machines to see and understand the visual world, is fundamental in advancing Artificial Intelligence.

Object recognition, which is defined as the task of locating and recognizing object categories in images and videos, is a major research field in computer vision. Recent research in object recognition …

A Pattern Language For Designing Application-Level Communication Protocols And The Improvement Of Computer Science Education Through Cloud Computing, Jorge Edison Lascano

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Enterprises that develop software use current technology because of its proven advantages and to accelerate and improve the software development process. Nevertheless, it is difficult to be up-to-date for most professionals in the area. Although students from higher academic institutions need to learn these new tools, and their main purpose is to learn how learn; colleges still need to prepare students for modern enterprise requirements, so they teach new technologies to improve students’ skills. Ubiquitous computing is software and services available everywhere, for example in mobile devices, in different locations, in different networks. This computing requires good communication protocols so …

Real-Time Vision-Based Lane Detection With 1d Haar Wavelet Transform On Raspberry Pi, Vikas Reddy Sudini

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Rapid progress is being made towards the realization of autonomous cars. Since the technology is in its early stages, human intervention is still necessary in order to ensure hazard-free operation of autonomous driving systems. Substantial research efforts are underway to enhance driver and passenger safety in autonomous cars. Toward that end GreedyHaarSpiker, a real-time vision-based lane detection algorithm is proposed for road lane detection in different weather conditions. The algorithm has been implemented in Python 2.7 with OpenCV 3.0 and tested on a Raspberry Pi 3 Model B ARMv8 1GB RAM coupled to a Raspberry Pi camera board v2. To …

High Fidelity Adaptive Cyber Emulation, Samir Mammadov

Theses and Dissertations

While looking for a high-level adaptive traffic generation tool, we came to realize that no such tool exists that can be used for rapid development while being platform agnostic. Having reviewed a wide array of tools to either implement user models or simulate traffic, we were unable to find a tool with the right capabilities while maintaining complexity, portability and extensibility. To overcome these issues, we introduce a new adaptive user-modelling framework for the specific use case of cyber activity emulation. Our framework supports the creation of high-level user models that can react to changes in their environments and vary …

Household Informedness And Policy Analytics For The Collection And Recycling Of Household Hazardous Waste In California, Kustini Lim-Wavde, Robert J. Kauffman, Gregory S. Dawson

Research Collection School Of Computing and Information Systems

Collection and recycling of household hazardous waste (HHW) can vary due to differences in household incomes, demographics, material recyclability, and HHW collection programs. We evaluate the role of household informedness, the degree to which households have the necessary information to make utility-maximizing decisions about the handling of their waste. Household informedness seems to be influenced by HHW public education and environmental quality information. We assess the effects of household informedness on HHW collection and recycling using panel data, community surveys, drinking water compliance reports, and census data in California from 2004 to 2012. The results enable the calculation of …

Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang

Research Collection School Of Computing and Information Systems

Social influence has attracted significant attention owing to the prevalence of social networks (SNs). In this paper, we study a new social influence problem, called personalized social influential tags exploration (PITEX), to help any user in the SN explore how she influences the network. Given a target user, it finds a size-k tag set that maximizes this user’s social influence. We prove the problem is NP-hard to be approximated within any constant ratio. To solve it, we introduce a sampling-based framework, which has an approximation ratio of 1−ǫ 1+ǫ with high probabilistic guarantee. To speedup the computation, we devise more …

Dynamic Nearest Neighbor Queries In Euclidean Space, Sarana Nutanong, Mohammed Eunus Ali, Egemen Tanin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L₂norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the k NN query result may change over a period of time due to changes in locations of the query point and/or data points.

Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Research Collection School Of Computing and Information Systems

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. …

A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim

Research Collection School Of Computing and Information Systems

This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …

Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly …