Computer Sciences Commons^™

A Mystery Of Human Biological Development -- Can It Be Used To Speed Up Computations?, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

For many practical problems, the only known algorithms for solving them require non-feasible exponential time. To make computations feasible, we need an exponential speedup. A reasonable way to look for such possible speedup is to search for real-life phenomena where such a speedup can be observed. A natural place to look for such a speedup is to analyze the biological activities of human beings -- since we, after all, solve many complex problems that even modern super-fast computers have trouble solving. Up to now, this search was not successful -- e.g., there are people who compute much faster than others, …

Automating Deep-Sea Video Annotation Using Machine Learning, Hanson Egbert, Lubomir Stanchev, Benjamin Ruttenberg

Computer Science and Software Engineering

As the world explores opportunities to develop offshore renewable energy capacity, there will be a growing need for pre-construction biological surveys and post-construction monitoring in the challenging marine environment. Underwater video is a powerful tool to facilitate such surveys, but the interpretation of the imagery is costly and time-consuming. Emerging technologies have improved automated analysis of underwater video, but these technologies are not yet accurate or accessible enough for widespread adoption in the scientific community or industries that might benefit from these tools. To address these challenges, we developed a website that allows us to: (1) Quickly play and annotate …

Establishing An Information System For Documenting Valuable Buildings By Using Gis In Egypt, Mona Mahrous Abdel Wahed

Emirates Journal for Engineering Research

Valuable heritage buildings are the history of nations, and history forms the identities of these nations. Many of these buildings are exposed to deterioration, destruction and distortion. Therefore, it is essential to protect and maintain these buildings to protect history. Effective documentation of valuable buildings is necessary to guide and assist stakeholders in making decisions regarding valuable buildings. Documentation requires robust and scientific methods. Therefore, it is important to utilize new technology in general and geographic information system GIS in particular in documenting valuable buildings. GIS has the potential to contribute and deal with valuable buildings at various stages and …

How To Gauge The Quality Of A Testing Method When Ground Truth Is Known With Uncertainty, Nicholas Gray, Scott Ferson, Vladik Kreinovich

Departmental Technical Reports (CS)

The quality of a testing method is usually measured by using sensitivity, specificity, and/or precision. To compute each of these three characteristics, we need to know the ground truth, i.e., we need to know which objects actually have the tested property. In many applications (e.g., in medical diagnostics), the information about the objects comes from experts, and this information comes with uncertainty. In this paper, we show how to take this uncertainty into account when gauging the quality of testing methods.

Stealthy And Efficient Adversarial Attacks Against Deep Reinforcement Learning, Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu

Research Collection School Of Computing and Information Systems

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to stealthily and efficiently attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to …

Experiential Learning Framework For Smaller Computer Science Programs, Zachary A. Kissel, Christopher S. Stuetzle

Computer and Data Science Faculty Publications

Experiential learning (EL) permeates the Computer Science discipline. This work seeks to codify EL practices for computer science pedagogy into ve key pillars. These pillars have been successfully applied at a small to mid-sized college within the heavily competitive Boston area. This paper further describes how a computer science department may eectively implement the pillars in their own curriculum.

Experiential Learning In The Technology Disciplines February 2020, Patricia Sendall, Christopher S. Stuetzle, Zachary A. Kissel, Tahir Hameed

Computer and Data Science Faculty Publications

Learning-by-doing has long been a tradition in the technology disciplines. It is the "hands-on" work, combined with student reflection, feedback and assessment, that reinforces theory into practice. Over the past 40 years, experiential learning (EL) in higher education has grown beyond in-class assignments to include internships, cooperative education, team-based learning, project-based learning, community engagement, service learning, international and study-away experiences, capstone projects and research opportunities. This paper provides an overview of experiential education theory and practice in the undergraduate technology disciplines, and presents examples of how experiential learning practices have evolved over time at a medium-sized institution in the Northeast …

Information Security Recommendation System, Vildan Bina

Theses and Dissertations

Konsiderimi gjithëpërfshirës i rreziqeve të TI-së rezulton nga një përdorim i shtuar i TI-së dhe ankesave dhe skandaleve që rezultojnë në fushën e teknologjisë së informacionit. Në veçanti, humbjet financiare që vijnë nga dëmtimi në reputacion dhe pretendimet për dëme kontribuojnë në një vetëdije të rritur për rreziqet themelore të TI.

Ky studim vë në pah disa prej shkaqeve të cilat bëjnë kanosjen e rrezikut ne sferen e sigurisë së informacionit,duke analizuar situatën nga ana e bizneseve në gjërat të cilat nuk u kushtojnë rëndësinë e merituar deri tek një zhvillues me qëllim të keq i cili tenton të bëj …

Cache-Enabled In Cooperative Cognitive Radio Networks For Transmission Performance, Jiachen Yang, Houbing Song, Chaofan Ma, Jiabao Man, Huifang Xu, Gan Zheng

Publications

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay …

The Changing Profile Of Ecs Teachers, Steven Mcgee, Lucia Dettori, Ronald I. Greenberg, Andrew M. Rasmussen, Dale F. Reed, Don Yanek

Computer Science: Faculty Publications and Other Works

This study compares the characteristics and professional development (PD) experiences between teachers who began teaching Exploring Computer Science before and after the enactment of a CS graduation requirement in the Chicago Public Schools. The post-requirement teachers were less likely to have a CS background, but their experience in the ECS PD and their level of confidence at the end of the PD were equivalent to the early adopters.

Algorithms Of Oppression [Uno Pa Theory Proseminar Presentation], Sue Ann Gardner

University of Nebraska-Lincoln Libraries: Presentations

Slides of two classes taught in the Theory Proseminar in the School of Public Administration at the University of Nebraska at Omaha by Sue Ann Gardner on February 11 and 18, 2020.

Connects information theory to applicable knowledge frameworks in public administration. Includes an in-depth discussion of the concepts addressed in Samiya Umoja Noble's book Algorithms of Oppression (published by New York University Press, New York, New York, United States, 2018) in the context of public administration and public academic libraries.

Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe

Research Collection School Of Computing and Information Systems

Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at …

Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Photorealistic multi-view face synthesis from a single image is an important but challenging problem. Existing methods mainly learn a texture mapping model from the source face to the target face. However, they fail to consider the internal deformation caused by the change of poses, leading to the unsatisfactory synthesized results for large pose variations. In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. Specifically, we propose a dual network that consists of two modules. The first module estimates the deformation of two views …

Geographic Difference Shaped Human Ocular Surface Metagenome Of Young Han Chinese From Beijing, Wenzhou, And Guangzhou Cities, Paul W. Bible, Yuhua Deng, Xiaofeng Wen, Xiao Hu, Et Al.

Computer Science Faculty publications

Purpose: Microbial ecosystems interact with the human body and affect human health. The microbial community on the ocular surface remains an underexplored territory despite its importance as the first line of defense barrier that protects the eye and ultimately sight. We investigated how age and sex affected human ocular surface microbiome, and in the present study wanted to understand how geographic difference shaped the microbiome in the ocular surface.

Methods: We collected conjunctival specimens of 172 eyes from 86 healthy volunteers living in three Chinese cities, namely, Guangzhou, Wenzhou, and Beijing. Using the direct metagenomic shotgun sequencing approach, we characterized …

Computer Science Through Concurrent Enrollment: A Strategy To Broaden Participation, Renee Fall, Seth Freeman, Ronald I. Greenberg, Dan Kaiser, Nigamanth Sridhar

Computer Science: Faculty Publications and Other Works

Most U.S. states support college-readiness and access through dual enrollment, in which high school students enroll in college courses. Concurrent enrollment (CE) allows students to take college courses in their own high school, taught by high school teachers approved by the partner college. CE has positive effects on students' education, but rarely is CS available through CE. Unlike AP, CE provides college credit to students who are assessed throughout the course rather than by a single high-stakes exam/project. This panel will showcase four different types of post-secondary institutions' experiences offering CS-through-CE and discuss its potential as an entry point into …

Defect-Free Plastic Deformation Through Dimensionality Reduction And Self-Annihilation Of Topological Defects In Crystalline Solids, Yipeng Gao, Yongfeng Zhang, Larry K. Aagesen, Jianguo Yu, Min Long, Yunzhi Wang

Computer Science Faculty Publications and Presentations

As a signature of symmetry-breaking processes, the generation and annihilation of topological defects (domain walls, strings, etc.) are of great interest in condensed matter physics and cosmology. Here we propose a distinctive self-organization process through phase transitions, in which all the generated topological defects are dimensionality reduced and self-annihilated. In crystalline solids, such a unique mechanism allows a perfect single crystal after plastic deformation, which originates from the coupling of different types of broken symmetries.

Color-Based Template Selection For Detection Of Gastric Abnormalities In Video Endoscopy, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani

Publications

Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, …

Interpretable Rumor Detection In Microblogs By Attending To User Interactions, Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang

Research Collection School Of Computing and Information Systems

We address rumor detection by learning to differentiate between the community’s response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) …

Deepbindiff: Learning Program-Wide Code Representations For Binary Diffing, Yue Duan, Xuezixiang Li, Jinghan Wang, Wang, Heng Yin

Research Collection School Of Computing and Information Systems

Binary diffing analysis quantitatively measures the differences between two given binaries and produces fine-grained basic block matching. It has been widely used to enable different kinds of critical security analysis. However, all existing program analysis and machine learning based techniques suffer from low accuracy, poor scalability, coarse granularity, or require extensive labeled training data to function. In this paper, we propose an unsupervised program-wide code representation learning technique to solve the problem. We rely on both the code semantic information and the program-wide control flow information to generate block embeddings. Furthermore, we propose a k-hop greedy matching algorithm to find …

Example-Based Colourization Via Dense Encoding Pyramids, Chufeng Xiao, Chu Han, Zhuming Zhang, Jing Qin, Tien-Tsin Wong, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

We propose a novel deep example-based image colourization method called dense encoding pyramid network. In our study, we define the colourization as a multinomial classification problem. Given a greyscale image and a reference image, the proposed network leverages large-scale data and then predicts colours by analysing the colour distribution of the reference image. We design the network as a pyramid structure in order to exploit the inherent multi-scale, pyramidal hierarchy of colour representations. Between two adjacent levels, we propose a hierarchical decoder–encoder filter to pass the colour distributions from the lower level to higher level in order to take both …

Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recognizing ingredients for a given dish image is at the core of automatic dietary assessment, attracting increasing attention from both industry and academia. Nevertheless, the task is challenging due to the difficulty of collecting and labeling sufficient training data. On one hand, there are hundred thousands of food ingredients in the world, ranging from the common to rare. Collecting training samples for all of the ingredient categories is difficult. On the other hand, as the ingredient appearances exhibit huge visual variance during the food preparation, it requires to collect the training samples under different cooking and cutting methods for robust …

Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen

Research Collection School Of Computing and Information Systems

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems, both in cloud applications and infrastructure layers. These problems are centered around empowering individually resource-limited devices to exhibit intelligent behavior, both in sensing and control, thanks to a judicious utilization of cloud resources. Cloud services will enable learning from data, performing inference, and executing control, all with assurances on outcomes. The paper discusses such emerging services and outlines five resulting new research directions towards enabling …

Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure …

Image Enhanced Event Detection In News Articles, Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juaizi Li, Lei Hou, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Event detection is a crucial and challenging sub-task of event extraction, which suffers from a severe ambiguity issue of trigger words. Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. We believe that images not only reflect the core events of the text, but are also helpful for the disambiguation of trigger words. In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct …

Automated Deprecated-Api Usage Update For Android Apps: How Far Are We?, Ferdian Thung, Stefanus Agus Haryono, Lucas Serrano, Gilles Muller, Julia Lawall, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

As the Android API evolves, some API methods may be deprecated, to be eventually removed. App developers face the challenge of keeping their apps up-to-date, to ensure that the apps work in both older and newer Android versions. Currently, AppEvolve is the state-of-the-art approach to automate such updates, and it has been shown to be quite effective. Still, the number of experiments reported is moderate, involving only API usage updates in 41 usage locations. In this work, we replicate the evaluation of AppEvolve and assess whether its effectiveness is generalizable. Given the set of APIs on which AppEvolve has been …

Joint Learning Of Answer Selection And Answer Summary Generation In Community Question Answering, Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, Ying Shen

Research Collection School Of Computing and Information Systems

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-Answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers …

Giving Voice To Parents In The Development Of The Preemie Prep For Parents (P3) Mobile App, Kris Pizur-Barnekow, Una Olivia Kim, Sheikh Iqbal Ahamed, Md Kamrul Hasan, Shannon Dreier, Steven R. Leuthner, Nicole Rau, Mir A. Basir

Computer Science Faculty Research and Publications

Background:

Parents at risk for preterm birth frequently receive prematurity education when the mother is hospitalized for premature labor. Parental ability to learn and consider the information is limited because of the stress of the hospitalization. A promising approach is dissemination of information to at-risk parents before the birth hospitalization.

Purpose:

This article describes formative research used to develop smartphone-based prematurity education app for parents at-risk for preterm birth.

Methods:

Stakeholders were parents with a prior preterm birth. Using stakeholder meeting transcripts, constant comparative analysis was used to reflect upon the parental voice.

Results:

The parents named the app, Preemie …

The Impact Of Cs For All On College Placement In Computer Science, Ronald I. Greenberg, Julie Medero, Samuel A. Rebelsky, Frances P. Trees, Dale Reed

Computer Science: Faculty Publications and Other Works

With the CS for All movement increasingly gaining traction nationally, students entering colleges and universities are arriving with deeper and broader CS experiences. This in turn can change students' higher education starting point. This panel of CS faculty with expertise in this area will present perspectives and models to describe how higher education choices for placement, credit, and curriculum design affect the efforts to broaden participation in student pathways into computing and related studies.

Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh

Research Collection School Of Computing and Information Systems

Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack of a quantitative guarantee about the quality of output approximate Nash equilibria (NE). A natural quantitative guarantee for such an approximate NE is the regret in the game (i.e. the best deviation gain). We formulate this deviation gain computation as a multi-armed bandit problem, with a new optimization goal unlike those studied in prior work. We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. We present sample complexity results as well as extensive …

Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman

Research Collection School Of Computing and Information Systems

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.