Computer Sciences Commons^™

A Novel Evolutionary Algorithm For Designing Robust Analog Filters, Shaobo Li, Wang Zou, Jianjun Hu

Faculty Publications

Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art …

Scaling Human Activity Recognition Via Deep Learning-Based Domain Adaptation, Md Abdullah Hafiz Khan, Nirmalya Roy, Archan Misra

Research Collection School Of Computing and Information Systems

We investigate the problem of making human activityrecognition (AR) scalable–i.e., allowing AR classifiers trainedin one context to be readily adapted to a different contextualdomain. This is important because AR technologies can achievehigh accuracy if the classifiers are trained for a specific individualor device, but show significant degradation when the sameclassifier is applied context–e.g., to a different device located ata different on-body position. To allow such adaptation withoutrequiring the onerous step of collecting large volumes of labeledtraining data in the target domain, we proposed a transductivetransfer learning model that is specifically tuned to the propertiesof convolutional neural networks (CNNs). Our model, …

A New Revocable And Re-Delegable Proxy Signature And Its Application, Shengmin Xu, Guomin Yang, Yi Mu

Research Collection School Of Computing and Information Systems

With the popularity of cloud computing and mobile Apps, on-demand services such as on-line music or audio streaming and vehicle booking are widely available nowadays. In order to allow efficient delivery and management of the services, for large-scale on-demand systems, there is usually a hierarchy where the service provider can delegate its service to a top-tier (e.g., countrywide) proxy who can then further delegate the service to lower level (e.g., region-wide) proxies. Secure (re-)delegation and revocation are among the most crucial factors for such systems. In this paper, we investigate the practical solutions for achieving re-delegation and revocation utilizing proxy …

Back Matter, Adfsl

Annual ADFSL Conference on Digital Forensics, Security and Law

No abstract provided.

Front Matter, Adfsl

Annual ADFSL Conference on Digital Forensics, Security and Law

No abstract provided.

Contents, Adfsl

Annual ADFSL Conference on Digital Forensics, Security and Law

No abstract provided.

Patent Keyword Extraction Algorithm Based On Distributed Representation For Patent Classification, Jie Hu, Shaobo Li, Yong Yao, Liya Yu, Guanci Yang, Jianjun Hu

Faculty Publications

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard …

An Uncertainty-Aware Workflow For Keyhole Surgery Planning Using Hierarchical Image Semantics, Christina Gillmann, Robin G.C. Maack, Tobias Post, Thomas Wischgoll, Hans Hagen

Computer Science and Engineering Faculty Publications

Keyhole surgeries become increasingly important in clinical daily routine as they help minimizing the damage of a patient's healthy tissue. The planning of keyhole surgeries is based on medical imaging and an important factor that influences the surgeries' success. Due to the image reconstruction process, medical image data contains uncertainty that exacerbates the planning of a keyhole surgery. In this paper we present a visual workfiow that helps clinicians to examine and compare different surgery paths as well as visualizing the patients' affected tissue. The analysis is based on the concept of hierarchical image semantics, that segment the underlying image …

Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu

Research Collection School Of Computing and Information Systems

The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE …

Risk-Sensitive Stochastic Orienteering Problems For Trip Optimization In Urban Environments, Pradeep Varakantham, Akshat Kumar, Hoong Chuin Lau, William Yeoh

Research Collection School Of Computing and Information Systems

Orienteering Problems (OPs) are used to model many routing and trip planning problems. OPs are a variantof the well-known traveling salesman problem where the goal is to compute the highest reward path thatincludes a subset of vertices and has an overall travel time less than a specified deadline. However, the applicabilityof OPs is limited due to the assumption of deterministic and static travel times. To that end, Campbellet al. extended OPs to Stochastic OPs (SOPs) to represent uncertain travel times (Campbell et al. 2011). Inthis article, we make the following key contributions: (1) We extend SOPs to Dynamic SOPs (DSOPs), …

Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers.The model is grounded in two multi-robot meeting and box pushing domains that …

Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah Daratan, Desmond Koh

Research Collection School Of Computing and Information Systems

In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers’ historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker’s trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures …

Resource-Constrained Scheduling For Maritime Traffic Management, Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of mitigating congestion and preventing hotspots in busy water areas such as Singapore Straits and port waters. Increasing maritime traffic coupled with narrow waterways makes vessel schedule coordination for just-in-time arrival critical for navigational safety. Our contributions are: 1) We formulate the maritime traffic management problem based on the real case study of Singapore waters; 2) We model the problem as a variant of the resource-constrained project scheduling problem (RCPSP), and formulate mixed-integer and constraint programming (MIP/CP) formulations; 3) To improve the scalability, we develop a combinatorial Benders (CB) approach that is significantly more effective than …

Dispatch Guided Allocation Optimization For Effective Emergency Response, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Effective emergency (medical, fire or criminal) response iscrucial for improving safety and security in urban environments. Recent research in improving effectiveness of emergency management systems (EMSs) has utilized data-drivenoptimization models for efficient allocation of emergency response vehicles (ERVs) to base locations. However, thesedata-driven optimization models either ignore the dispatchstrategy of ERVs (typically the nearest available ERV is dispatched to serve an incident) or employ myopic approaches(e.g., greedy approach based on marginal gain). This resultsin allocations that are not synchronised with the real evolution dynamics on the ground or can be improved significantly.To bridge this gap, we make the following contributions: …

Scheduling In Mapreduce Clusters, Chen He

School of Computing: Dissertations, Theses, and Student Research

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing.

As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied.

The objective of this dissertation is to …

Internet Of Underground Things: Sensing And Communications On The Field For Precision Agriculture, Mehmet C. Vuran, Abdul Salam, Rigoberto Wong, Suat Irmak

School of Computing: Conference and Workshop Papers

The projected increases in World population and need for food have recently motivated adoption of information technology solutions in crop fields within precision agriculture approaches. Internet of underground things (IOUT), which consists of sensors and communication devices, partly or completely buried underground for real-time soil sensing and monitoring, emerge from this need. This new paradigm facilitates seamless integration of underground sensors, machinery, and irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. In this paper, state-of-the-art communication architectures are reviewed, and underlying sensing technology and communication mechanisms for IOUT are presented. Recent advances in the …

Vision-Based Assistive Indoor Localization, Feng Hu

Dissertations, Theses, and Capstone Projects

An indoor localization system is of significant importance to the visually impaired in their daily lives by helping them localize themselves and further navigate an indoor environment. In this thesis, a vision-based indoor localization solution is proposed and studied with algorithms and their implementations by maximizing the usage of the visual information surrounding the users for an optimal localization from multiple stages. The contributions of the work include the following: (1) Novel combinations of a daily-used smart phone with a low-cost lens (GoPano) are used to provide an economic, portable, and robust indoor localization service for visually impaired people. (2) …

Trust In Visualization (And What It Has To Do With Theory), Thomas Wischgoll

Computer Science and Engineering Faculty Publications

There are different issues with trust involved when working with domain experts to visualize their data. There may be limitations with the data that require special precautions, such as sensitivity or security limitations. It may have taken a lot of effort to collect or create the data so that a certain level of trust is required for the domain expert to share the data. At the same time, the domain expert needs to be able to trust in the final visualization results. This presentation discusses these issues with trust and what requirements for a theoretical foundation this results in. Furthermore, …

A Hierarchical Feature Extraction Model For Multi-Label Mechanical Patent Classification, Jie Hu, Shaobo Li, Jianjun Hu, Guanci Yang

Faculty Publications

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to …

A Fast And Robust Extrinsic Calibration For Rgb-D Camera Networks, Po-Chang Su, Ju Shen, Wanxin Xu, Sen-Ching S. Cheung, Ying Luo

Electrical and Computer Engineering Faculty Publications

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope …

Computer Program, System, And Method For Observation And Communication For Mobile Settings, Mobile Applications, And Wearable Mobile Devices, Jibo He, Barbara Chaparro

Publications

A system including at least first and second wearable mobile devices and optionally one or more smartphones or other computing devices for allowing a wearable mobile device wearer, an on-site observer, and a remote observer to research and test usability of products in mobile settings, mobile applications, mobile devices, and wearable mobile devices, desktop usability settings, and other settings and devices. The devices nm a software application for generating first-person video and third-person video, transmitting the video to the other devices, marking the videos with time stamps, and allowing the remote observer to send messages and other information to the …

An Overview Of The Usage Of Default Passwords, Brandon Knierem, Xiaolu Zhang, Philip Levine, Frank Breitinger, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

The recent Mirai botnet attack demonstrated the danger of using default passwords and showed it is still a major problem. In this study we investigated several common applications and their password policies. Specifically, we analyzed if these applications: (1) have default passwords or (2) allow the user to set a weak password (i.e., they do not properly enforce a password policy). Our study shows that default passwords are still a significant problem: 61% of applications inspected initially used a default or blank password. When changing the password, 58% allowed a blank password, 35% allowed a weak password of 1 character.

Comparing And Enhancing The Analytical Model For Exposure Of A Retail Facility Layout With Human Performance, Bradley R. Guthrie, Pratik Parikh, Tyler Whitlock, Madison Glines, Thomas Wischgoll, John Flach, Scott Watamaniuk

Computer Science and Engineering Faculty Publications

Recent research in retail facility layout has focused on developing analytical models to estimate visibility measures of novel rack layouts based on assumptions about a shopper’s field of view. However, because of the human element involved in the shopping experience, it is vital to compare these models relative to actual human performance. In this study, we evaluate the predictions of our previously developed analytical model (that estimates exposure of every location on a given rack layout assuming expected head movement) in a 3D Virtual Environment (VE). We conducted trials with 18 participants who were asked to find targets strategically placed …

Implementation Of Unmanned Aerial Vehicles Reporting Plume Cloud Concentration Values In A 3d Simulation Environment, Emily Catherine Novak

Browse all Theses and Dissertations

Unmanned aerial vehicles, or UAVs, have the potential to vastly improve plume cloud tracking at low cost. Plume clouds can be produced from blast mining, chemical warfare, unintended man-made disasters, and natural causes. This thesis provides implementation of the capability to simulate a 3D environment in which UAVs are individually controlled and each report a plume's concentration value at a specific location. It leverages existing industry standard technologies, including the PX4 autopilot system, the Gazebo simulation environment, the Robot Operating System (ROS), and QGroundControl. The provided system integrates the existing tools with a plume model plug-in that provides simulated plume …

Threats And Mitigation Of Ddos Cyberattacks Against The U.S. Power Grid Via Ev Charging, Glenn Sean Morrison

Browse all Theses and Dissertations

Cars are an ever changing and integral part of modern society. Two of the biggest changes in vehicles today are their heavy integration with wireless communication and the push toward battery powered Electric Vehicles (EV). EV and EV charging stations have become a part of the Internet of Things (IoT). While this connectedness increases the convenience and functionality of the vehicles and charging stations, it also opens them up to a wide range of cyber threats. This thesis examines the potential threats against the EV charging ecosystem through a historical analysis of past cyberattacks and identified vulnerabilities. As EV charging …

Malware Analysis Skills Taught In University Courses, Swetha Gorugantu

Browse all Theses and Dissertations

Career opportunities for malware analysts are growing at a fast pace due to the evolving nature of cyber threats as well as the necessity to counter them. However, employers are often unable to hire analysts fast though due to a lack of the required skillset. Hence, the primary purpose of the thesis is to conduct a gap analysis between the binary analysis skills taught in universities with those that the recruiters are looking for. Malware can be analyzed using three main types of tools and techniques: high-level profiling, static analysis, and dynamic analysis. These methods provide detailed information about the …

Automating The Crowd-Mapping Workflow With Deep Learning, Lasith Niroshan

Theses

Maintaining updated maps in an ever-changing built environment is important for supporting modern society in many ways. The usage of online crowdsourced maps in particular has gained importance in a wide range of recent location-based applications (route planning/navigation, urban planning, real estate, tourism, etc). However, both traditional map production methods and updating today’s online maps suffer from early obsolescence due to their largely manual map production/update workflows. Significant research efforts have focused on refining techniques to identify changes in raster satellite images, aiming to improve and streamline map production processes. Concurrently, the surge in Internet usage has led to a …

Multi-Scale And Multi-Modal Streaming Data Aggregation And Processing For Decision Support During Natural Disasters, Shruti Kar

Browse all Theses and Dissertations

With the surge in digital information systems, there is a data deluge from various sources that can be analyzed and integrated to produce relevant, reliable and actionable information, for better decision making. We employ multi-modal data (i.e., unstructured text, gazetteers, and imagery) for an aggregate level analysis and location-centric demand/request matching in the context of disaster relief. After classifying the Need expressed in a tweet (the WHAT), we leverage OpenStreetMap to geolocate that Need on a computationally accessible map of the local terrain (the WHERE) populated with location features such as hospitals and housing. Further, our novel use of flood …

Development Of An Ios App For Learning Intonation Of Wind Instruments, Swathi Pamidi

Browse all Theses and Dissertations

Learning music instrument is a challenging task for a beginner without constant guidance from an instructor. The primary objective of this thesis research is to design and develop an iOS mobile / iPad learning app that helps users to learn and practice intonation for a suite of wind instruments by themselves with comfort and ease through app-provided tuning and charting guidance and app-assisted self-assessment. Particularly, our successfully-implemented app provides the following features to enhance the user's learning experience: 1 ) Provides learners easy-to-access information for the fingering and tuning techniques of wind instruments by converting Dr. Shelley Jagow's book - …

Hurricane Evacuation Modeling Using Behavior Models And Scenario-Driven Agent-Based Simulations, Yuan Zhu, Kun Xie, Kaan Ozbay, Hong Yang

Computational Modeling & Simulation Engineering Faculty Publications

Transportation modeling and simulation play an important role in the planning and management of emergency evacuation. It is often indispensable for the preparedness and timely response to extreme events occurring in highly populated areas. Reliable and robust agent-based evacuation models are of great importance to support evacuation decision making. Nevertheless, these models rely on numerous hypothetical causal relationships between the evacuation behavior and a variety of factors including socio-economic characteristics and storm intensity. Understanding the impacts of these factors on evacuation behaviors (e.g., destination and route choices) is crucial in preparing optimal evacuation plans. This paper aims to contribute to …