Computer Sciences Commons^™

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

Parallelization Of Molecular Docking Algorithms Using Cuda For Use In Drug Discovery, Brandon Stewart, Jonathan Fine, Gaurav Chopra Phd

The Summer Undergraduate Research Fellowship (SURF) Symposium

Traditional drug discovery methodology uses a multitude of software packages to design and evaluate new drug-like compounds. While software packages implement a wide variety of methods, the serial (i.e. single core) implementation for many of these algorithms, prohibit large scale docking, such as proteome-wide docking (i.e. thousands of compounds with thousands of proteins). Several docking algorithms can be parallelized, significantly reducing the runtime of the calculations, thus enabling large-scale docking. Implementing algorithms that take advantage of the distributed nature of graphical processing units (GPUs) via the Compute Unified Device Architecture (CUDA) enables us to efficiently implement massively parallel algorithms. Two …

Investigating Genetic Algorithm Optimization Techniques In Video Games, Nathan Ambuehl

Undergraduate Honors Theses

Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with …

Vertex Weighted Spectral Clustering, Mohammad Masum

Electronic Theses and Dissertations

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to …

Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu

Research Collection School Of Computing and Information Systems

Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, …

Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang

Research Collection School Of Computing and Information Systems

Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …

Projection-Free Distributed Online Learning In Networks, Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang

Research Collection School Of Computing and Information Systems

The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems. However, none of existing studies has explored it in the distributed online learning setting, where locally light computation is assumed. In this paper, we fill this gap by proposing the distributed online conditional gradient algorithm, which eschews the expensive projection operation needed in its counterpart algorithms by exploiting much simpler linear optimization steps. We give a regret bound for the proposed algorithm as a function of the network size and topology, which will be smaller …

Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar

Research Collection School Of Computing and Information Systems

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …

Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform are build of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing …

Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu

Research Collection School Of Computing and Information Systems

Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations …

Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose …

An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert J. Ross

Conference papers

Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette from both theoretical and empirical perspectives. The theoretical analysis shows that Simplified Silhouette has a mathematical relationship with both the k-means Cost Function and the original Silhouette, while empirically, we show that …

A Decidable Fragment In Separation Logic With Inductive Predicates And Arithmetic, Quang Loc Le, Makoto Tatsuta, Jun Sun, Wei-Ngan Chin

Research Collection School Of Computing and Information Systems

We consider the satisfiability problem for a fragment of separation logic including inductive predicates with shape and arithmetic properties. We show that the fragment is decidable if the arithmetic properties can be represented as semilinear sets. Our decision procedure is based on a novel algorithm to infer a finite representation for each inductive predicate which precisely characterises its satisfiability. Our analysis shows that the proposed algorithm runs in exponential time in the worst case. We have implemented our decision procedure and integrated it into an existing verification system. Our experiment on benchmarks shows that our procedure helps to verify the …

Educational Magic Tricks Based On Error-Detection Schemes, Ronald I. Greenberg

Computer Science: Faculty Publications and Other Works

Magic tricks based on computer science concepts help grab student attention and can motivate them to delve more deeply. Error detection ideas long used by computer scientists provide a rich basis for working magic; probably the most well known trick of this type is one included in the CS Unplugged activities. This paper shows that much more powerful variations of the trick can be performed, some in an unplugged environment and some with computer assistance. Some of the tricks also show off additional concepts in computer science and discrete mathematics.

A Weighted Maximum Matching Algorithm For Influence Maximization And Structural Controllability, Giorgio Sartor, Yeow Khiang Chia, Laura Wynter, Justin Ruths

Research Collection School Of Computing and Information Systems

Structural control and influence maximization on networks both admit the problem of selecting a particular subset of nodes. In structural control, the subset of nodes should guarantee the controllability of the network (in the usual sense) for almost any combination of weights. In influence maximization, given a diffusion process over the network, the chosen subset of nodes (of a given cardinality) should produce the greatest diffusive influence over the rest of the network. While structural control exploits only the structure of the network, influence maximization depends both on the structure and the weights of the edges. We modify an algorithm …

Game Specific Approaches To Monte Carlo Tree Search For Dots And Boxes, Jared Prince

Mahurin Honors College Capstone Experience/Thesis Projects

In this project, a Monte Carlo tree search player was designed and implemented for the child’s game dots and boxes, the computational burden of which has left traditional artificial intelligence approaches like minimax ineffective. Two potential improvements to this player were implemented using game-specific information about dots and boxes: the lack of information for decision-making provided by the net score and the inherent symmetry in many states. The results of these two approaches are presented, along with details about the design of the Monte Carlo tree search player. The first improvement, removing net score from the state information, was proven …

Encryption Backdoors: A Discussion Of Feasibility, Ethics, And The Future Of Cryptography, Jennifer A. Martin

Honors Projects

In the age of technological advancement and the digitization of information, privacy seems to be all but an illusion. Encryption is supposed to be the white knight that keeps our information and communications safe from unwanted eyes, but how secure are the encryption algorithms that we use? Do we put too much trust in those that are charged with implementing our everyday encryption systems? This paper addresses the concept of backdoors in encryption: ways that encryption systems can be implemented so that the security can be bypassed by those that know about its existence. Many governments around the world are …

Solving Algorithmic Problems In Finitely Presented Groups Via Machine Learning, Jonathan Gryak

Dissertations, Theses, and Capstone Projects

Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this dissertation, we seek to extend these techniques to finitely presented non-free groups, in particular to polycyclic and metabelian groups that are of interest to non-commutative cryptography.

As a prototypical example, we utilize supervised learning methods to construct classifiers that can solve the conjugacy decision problem, i.e., determine whether or not a pair of elements from a specified group are conjugate. The accuracies of classifiers created using decision trees, random forests, and N-tuple neural network models are evaluated for several non-free groups. …

Neural Network Ai For Fightingice, Alan D. Robison

Computer Engineering

Game AI in the fighting game genre, along the lines of Street Fighter, Mortal Kombat and Tekken, is traditionally script-based, with hard-coded reactions to various situations. Though this approach is often easy to understand and tweak, it requires substantial time and understanding of the game to implement in a way that is challenging and satisfying for the player due to the very large possibility space. This paper explores the use of neural networks as an alternative approach by implementing and training a network to select an action to take each frame based on the game state.

Local Gaussian Processes For Efficient Fine-Grained Traffic Speed Prediction, Truc Viet Le, Richard Oentaryo, Siyuan Liu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Traffic speed is a key indicator for the efficiency of an urban transportation system. Accurate modeling of the spatiotemporally varying traffic speed thus plays a crucial role in urban planning and development. This paper addresses the problem of efficient fine-grained traffic speed prediction using big traffic data obtained from static sensors. Gaussian processes (GPs) have been previously used to model various traffic phenomena, including flow and speed. However, GPs do not scale with big traffic data due to their cubic time complexity. In this work, we address their efficiency issues by proposing localGPs to learn from and make predictions for …

Tackling Large-Scale Home Health Care Delivery Problem With Uncertainty, Cen Chen, Zachary Rubinstein, Stephen Smith, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we investigate a multi-period Home HealthCare Scheduling Problem (HHCSP) under stochastic serviceand travel times. We first model the deterministic problemas an integer linear programming model that incorporatesreal-world requirements, such as time windows, continuityof care, workload fairness, inter-visit temporal dependencies.We then extend the model to cope with uncertainty in durations,by introducing chance constraints into the formulation.We propose efficient solution approaches, which providequantifiable near-optimal solutions and further handlethe uncertainties by employing a sampling-based strategy. Wedemonstrate the effectiveness of our proposed approaches oninstances synthetically generated by real-world dataset forboth deterministic and stochastic scenarios.

Sampling Based Approaches For Minimizing Regret In Uncertain Markov Decision Problems (Mdps), Asrar Ahmed, Pradeep Varakantham, Meghna Lowalekar, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Markov Decision Processes (MDPs) are an effective model to represent decision processes in the presence of transitional uncertainty and reward tradeoffs. However, due to the difficulty in exactly specifying the transition and reward functions in MDPs, researchers have proposed uncertain MDP models and robustness objectives in solving those models. Most approaches for computing robust policies have focused on the computation of maximin policies which maximize the value in the worst case amongst all realisations of uncertainty. Given the overly conservative nature of maximin policies, recent work has proposed minimax regret as an ideal alternative to the maximin objective for robust …

Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu

Electrical & Computer Engineering Faculty Publications

This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of …

Compress: A Comprehensive Framework Of Trajectory Compression In Road Networks, Yunheng Han, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

More and more advanced technologies have become available to collect and integrate an unprecedented amount of data from multiple sources, including GPS trajectories about the traces of moving objects. Given the fact that GPS trajectories are vast in size while the information carried by the trajectories could be redundant, we focus on trajectory compression in this article. As a systematic solution, we propose a comprehensive framework, namely, COMPRESS (Comprehensive Paralleled Road-Network-Based Trajectory Compression), to compress GPS trajectory data in an urban road network. In the preprocessing step, COMPRESS decomposes trajectories into spatial paths and temporal sequences, with a thorough justification …

Cataloging Github Repositories, Abhishek Sharma, Ferdian Thung, Pavneet Singh Kochhar, Agus Sulistya, David Lo

Research Collection School Of Computing and Information Systems

GitHub is one of the largest and most popular repository hosting service today, having about 14 million users and more than 54 million repositories as of March 2017. This makes it an excellent platform to find projects that developers are interested in exploring. GitHub showcases its most popular projects by cataloging them manually into categories such as DevOps tools, web application frameworks, and game engines. We propose that such cataloging should not be limited only to popular projects. We explore the possibility of developing such cataloging system by automatically extracting functionality descriptive text segments from readme files of GitHub repositories. …

Community Detection In Social Networks, Ketki Kulkarni

Master's Projects

The rise of the Internet has brought people closer. The number of interactions between people across the globe has gone substantially up due to social awareness, the advancements of the technology, and digital interaction. Social networking sites have built societies, communities virtually. Often these societies are displayed as a network of nodes depicting people and edges depicting relationships, links. This is a good and e cient way to store, model and represent systems which have a complex and rich information. Towards that goal we need to nd e ective, quick methods to analyze social networks. One of the possible solution …

Influence Detection And Spread Estimation In Social Networks, Madhura Kaple

Master's Projects

A social network is an online platform, where people communicate and share information with each other. Popular social network features, which make them di erent from traditional communication platforms, are: following a user, re-tweeting a post, liking and commenting on a post etc. Many companies use various social networking platforms extensively as a medium for marketing their products. A xed amount of budget is alloted by the companies to maximize the positive in uence of their product. Every social network consists of a set of users (people) with connections between them. Each user has the potential to extend its in …

An Improved Algorithm For Learning To Perform Exception-Tolerant Abduction, Mengxue Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Abstract

Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect, …

Algorithmic Factorization Of Polynomials Over Number Fields, Christian Schulz

Mathematical Sciences Technical Reports (MSTR)

The problem of exact polynomial factorization, in other words expressing a polynomial as a product of irreducible polynomials over some field, has applications in algebraic number theory. Although some algorithms for factorization over algebraic number fields are known, few are taught such general algorithms, as their use is mainly as part of the code of various computer algebra systems. This thesis provides a summary of one such algorithm, which the author has also fully implemented at https://github.com/Whirligig231/number-field-factorization, along with an analysis of the runtime of this algorithm. Let k be the product of the degrees of the adjoined elements used …

Unsupervised Machine Learning In Agent-Based Modeling, Luke D. Robinson

Celebration of Learning

Agent-based models (ABMs) are used by researchers in a variety of fields to model natural phenomena. In an ABM, a wide range of behaviors and outcomes can be observed based on the parameters of the model. In many cases, these behaviors can be categorized into discrete outcomes identifiable by human observers. Our goal was to use clustering algorithms to identify those outcomes from model output data. For this project, we used data from the NetLogo Wolf Sheep Predation model to explore and evaluate three clustering algorithms from Python's scikit-learn package. If this task can be completed reliably by a computer, …