Computer Sciences Commons^™

Gartner's Hype Cycle: A Simple Explanation, Jose Perez, Vladik Kreinovich

Departmental Technical Reports (CS)

In the ideal world, any innovation should be gradually accepted. It is natural that initially some people are reluctant to adopt a new largely un-tested idea, but as more and more evidence appears that this new idea works, we should see a gradual increase in number of adoptees -- until the idea becomes universally accepted.

In real life, the adoption process is not that smooth. Usually, after the few first successes, the idea is over-hyped, it is adopted in situations way beyond the inventors' intent. In these remote areas, the new idea does not work well, so we have a …

Working On One Part At A Time Is The Best Strategy For Software Production A Proof, Francisco Zapata, Maliheh Zargaran, Vladik Kreinovich

Departmental Technical Reports (CS)

When a company works on a large software project, it can often start recouping its investments by selling intermediate products with partial functionality. With this possibility in mind, it is important to schedule work on different software parts so as to maximize the profit. These exist several algorithms for solving the corresponding optimization problem, and in all the resulting plans, at each moment of time, we work on one part of software at a time. In this paper, we prove that this one-part-at-a-time property holds for all optimal plans.

Why Superforecasters Change Their Estimates On Average By 3.5%: A Possible Theoretical Explanation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent large-scale study of people's forecasting ability has shown that there is a small group of superforecasters, whose forecasts are significantly more accurate than the forecasts of an average person. Since forecasting is important in many application areas, researchers have studied what exactly the supreforecasters do differently -- and how we can learn from them, so that we will be able to forecast better. One empirical fact that came from this study is that, in contrast to most people, superforecasters make much smaller adjustments to their probability estimates. On average, their average probability change is 3.5%. In this …

Kratylos: A Tool For Sharing Interlinearized And Lexical Data In Diverse Formats, Daniel Kaufman, Raphael Finkel

Computer Science Faculty Publications

In this paper we present Kratylos, at www.kratylos.org/, a web application that creates searchable multimedia corpora from data collections in diverse formats, including collections of interlinearized glossed text (IGT) and dictionaries. There exists a crucial lacuna in the electronic ecology that supports language documentation and linguistic research. Vast amounts of IGT are produced in stand-alone programs without an easy way to share them publicly as dynamic databases. Solving this problem will not only unlock an enormous amount of linguistic information that can be shared easily across the web, it will also improve accountability by allowing us to verify analyses …

The Cybher Program Supported By Cisse Framework To Engage And Anchor Middle-School Girls In Cybersecurity, Pamela Rowland

Masters Theses & Doctoral Dissertations

There is a piercing shortage of personnel in the cybersecurity field that will take several decades to accommodate. Despite being 50 percent of the workforce, females only account for 11 percent of the cybersecurity personnel. While efforts have been made to encourage more females into the field, more needs to be done. Reality shows that a change in the statistics is not taking place. Women remain seriously under-represented in cybersecurity degree programs and the workforce.

Prior research shows that elementary girls are equally as interested in the cyber path as boys. It is in middle school that this interest shifts, …

A Capability-Centric Approach To Cyber Risk Assessment And Mitigation, Thomas H. Llansó

Masters Theses & Doctoral Dissertations

Cyber-enabled systems are increasingly ubiquitous and interconnected, showing up in traditional enterprise settings as well as increasingly diverse contexts, including critical infrastructure, avionics, cars, smartphones, home automation, and medical devices. Meanwhile, the impact of cyber attacks against these systems on our missions, business objectives, and personal lives has never been greater. Despite these stakes, the analysis of cyber risk and mitigations to that risk tends to be a subjective, labor-intensive, and costly endeavor, with results that can be as suspect as they are perishable. We identified the following gaps in those risk results: concerns for (1) their repeatability/reproducibility, (2) the …

A Shoulder Surfing Resistant Graphical Authentication System, Hung-Min Sun, Shiuan-Tung Chen, Jyh-Haw Yeh, Chia-Yun Cheng

Computer Science Faculty Publications and Presentations

Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as ”the weakest link” in the authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and anywhere with various devices. This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording …

Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen

Research Collection School Of Computing and Information Systems

We present Automatic Persona Generation (APG), a methodology and system for quantitative persona generation using large amounts of online social media data. The system is operational, beta deployed with several client organizations in multiple industry verticals and ranging from small-to-medium sized enterprises to large multi-national corporations. Using a robust web framework and stable back-end database, APG is currently processing tens of millions of user interactions with thousands of online digital products on multiple social media platforms, such as Facebook and YouTube. APG identifies both distinct and impactful user segments and then creates persona descriptions by automatically adding pertinent features, such …

An Empirical Study Of Css Code Smells In Web Frameworks, Tobias Paul Bleisch

Master's Theses

Cascading Style Sheets (CSS) has become essential to front-end web development for the specification of style. But despite its simple syntax and the theoretical advantages attained through the separation of style from content and behavior, CSS authoring today is regarded as a complex task. As a result, developers are increasingly turning to CSS preprocessor languages and web frameworks to aid in development. However, previous studies show that even highly popular websites which are known to be developed with web frameworks contain CSS code smells such as duplicated rules and hard-coded values. Such code smells have the potential to cause adverse …

A Large-Scale Analysis Of How Openssl Is Used In Open-Source Software, Scott Jared Heidbrink

Theses and Dissertations

As vulnerabilities become more common the security of applications are coming under increased scrutiny. In regards to Internet security, recent work discovers that many vulnerabilities are caused by TLS library misuse. This misuse is attributed to large and confusing APIs and developer misunderstanding of security generally. Due to these problems there is a desire for simplified TLS libraries and security handling. However, as of yet there is no analysis of how the existing APIs are used, beyond how incorrect usage motivates the need to replace them. We provide an analysis of contemporary usage of OpenSSL across 410 popular secure applications. …

Nocloud: Experimenting With Network Disconnection By Design, Reza Rawassizadeh, Timothy Pierson, Ronald Peterson, David Kotz

Dartmouth Scholarship

Application developers often advocate uploading data to the cloud for analysis or storage, primarily due to concerns about the limited computational capability of ubiquitous devices. Today, however, many such devices can still effectively operate and execute complex algorithms without reliance on the cloud. The authors recommend prioritizing on-device analysis over uploading the data to another host, and if on-device analysis is not possible, favoring local network services over a cloud service.

Towards Practical Privacy-Preserving Analytics For Iot And Cloud Based Healthcare Systems, Sagar Sharma, Keke Chen, Amit P. Sheth

Kno.e.sis Publications

Modern healthcare systems now rely on advanced computing methods and technologies, such as IoT devices and clouds, to collect and analyze personal health data at unprecedented scale and depth. Patients, doctors, healthcare providers, and researchers depend on analytical models derived from such data sources to remotely monitor patients, early-diagnose diseases, and find personalized treatments and medications. However, without appropriate privacy protection, conducting data analytics becomes a source of privacy nightmare. In this paper, we present the research challenges in developing practical privacy-preserving analytics in healthcare information systems. The study is based on kHealth - a personalized digital healthcare information system …

Rpp Panel Chicago: History Of Cafecs, Lucia Dettori, Dale Reed, Steven Mcgee, Don Yanek, Andrew Rasmussen, Ronald Greenberg

Computer Science: Faculty Publications and Other Works

No abstract provided.

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 …

Auxetic Deformations And Elliptic Curves, Ciprian S. Borcea, Ileana Streinu

Computer Science: Faculty Publications

The problem of detecting auxetic behavior, originating in materials science and mathematical crystallography, refers to the property of a flexible periodic bar-and-joint framework to widen, rather than shrink, when stretched in some direction. The only known algorithmic solution for detecting infinitesimal auxeticity is based on the rather heavy machinery of fixed-dimension semi-definite programming. In this paper we present a new, simpler algorithmic approach which is applicable to a natural family of 3D periodic bar-and-joint frameworks with 3 degrees-of-freedom. This class includes most zeolite structures, which are important for applications in computational materials science. We show that the existence of auxetic …

Virtualized Cloud Platform Management Using A Combined Neural Network And Wavelet Transform Strategy, Chunyu Liu

Electronic Theses, Projects, and Dissertations

This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural …

Using Autoencoder To Reduce The Length Of The Autism Diagnostic Observation Schedule (Ados), Sara Hussain Daghustani

Electronic Theses, Projects, and Dissertations

This thesis uses autoencoders to explore the possibility of reducing the length of the Autism Diagnostic Observation Schedule (ADOS), which is a series of tests and observations used to diagnose autism spectrum disorders in children, adolescents, and adults of different developmental levels. The length of the ADOS, directly and indirectly, causes barriers to its access for many individuals, which means that individuals who need testing are unable to get it. Reducing the length of the ADOS without significantly sacrificing its accuracy would increase its accessibility. The autoencoders used in this thesis have specific connections between layers that mimic the sectional …

Target Detection Using Convolutional Neural Networks, Robert P. Loibl

Theses and Dissertations

This research explores the use of Convolutional Neural Networks (CNNs) to classify targets of interest within satellite imagery. Methods were specifically devised for the classification of airports within Landsat-8 scenes. A novel automated dataset generation technique was developed to create labeled datasets from satellite imagery using only coordinate metadata. Using this approach a very large dataset of over 132,000 labeled images was created without human input. This dataset was used to evaluate the effects of color and resolution on airport classification accuracy. Two experiments were run with the first experiment classifying large airports with 96.8% accuracy, and the second classifying …

Https://Onlinelibrary.Wiley.Com/Doi/10.1002/Spy2.15#:~:Text=A%20review%20and%20an%20empirical%20analysis%20of%20privacy%20policy%20and%20notices%20for%20consumer%20internet%20of%20things, Alfredo J. Perez, Sherali Zeadally, Jonathan Cochran

Computer Science Faculty Publications

The privacy policies and practices of six consumer Internet of things (IoT) devices were reviewed and compared. In addition, an empirical verification of the compliance of privacy policies for data collection practices on two voice-activated intelligent assistant devices, namely the Amazon Echo Dot and Google Home devices was performed. The review shows that IoT privacy policies may not be usable from the human-computer interaction perspective because IoT policies are included as part of the manufacturers' general privacy policy (which may include policies unrelated to the device), or the IoT policy requires to read (in addition to the IoT policies) the …

W-Air: Enabling Personal Air Pollution Monitoring On Wearables, Balz Maag, Zimu Zhou, Lothar Thiele

Research Collection School Of Computing and Information Systems

Accurate, portable and personal air pollution sensing devices enable quantification of individual exposure to air pollution, personalized health advice and assistance applications. Wearables are promising (e.g., on wristbands, attached to belts or backpacks) to integrate commercial off-the-shelf gas sensors for personal air pollution sensing. Yet previous research lacks comprehensive investigations on the accuracies of air pollution sensing on wearables. In response, we proposed W-Air, an accurate personal multi-pollutant monitoring platform for wearables. We discovered that human emissions introduce non-linear interference when low-cost gas sensors are integrated into wearables, which is overlooked in existing studies. W-Air adopts a sensor-fusion calibration scheme …

Building Trust In Artificial Intelligence, Machine Learning, And Robotics, Keng Siau, Weiyu Wang

Research Collection School Of Computing and Information Systems

In this article, we look at trust in artificial intelligence, machine learning (ML), and robotics. We first review the concept of trust in AI and examine how trust in AI may be different from trust in other technologies. We then discuss the differences between interpersonal trust and trust in technology and suggest factors that are crucial in building initial trust and developing continuous trust in artificial intelligence.

The Way You Move: The Effect Of A Robot Surrogate Movement In Remote Collaboration, Martin Feick, Lora Oehlberg, Anthony Tang, André Miede, Ehud Sharlin

Research Collection School Of Computing and Information Systems

In this paper, we discuss the role of the movement trajectory and velocity enabled by our tele-robotic system (ReMa) for remote collaboration on physical tasks. Our system reproduces changes in object orientation and position at a remote location using a humanoid robotic arm. However, even minor kinematics differences between robot and human arm can result in awkward or exaggerated robot movements. As a result, user communication with the robotic system can become less efficient, less fluent and more time intensive.

Education In The Age Of Artificial Intelligence: How Will Technology Shape Learning?, Keng Siau

Research Collection School Of Computing and Information Systems

The age of Artificial Intelligence (AI) is here! Higher education needs to prepare students for a world in which AI plays an increasingly dominant role. What are the jobs that can be replaced easily? How would higher education be affected in the age of AI, robotics, machine learning, and automation? How can higher education excel and flourish in the age of AI?

Vmkdo: Verifiable Multi-Keyword Search Over Encrypted Cloud Data For Dynamic Data-Owner, Yibin Miao, Jianfeng Ma, Ximeng Liu, Zhiquan Liu, Limin Shen, Fushan Wei

Research Collection School Of Computing and Information Systems

The advantages of cloud computing encourage individuals and enterprises to outsource their local data storage and computation to cloud server, however, data security and privacy concerns seriously hinder the practicability of cloud storage. Although searchable encryption (SE) technique enables cloud server to provide fundamental encrypted data retrieval services for data-owners, equipping with a result verification mechanism is still of prime importance in practice as semi-trusted cloud server may return incorrect search results. Besides, single keyword search inevitably incurs many irrelevant results which result in waste of bandwidth and computation resources. In this paper, we are among the first to tackle …

Do Your Friends Make You Buy This Brand?: Modeling Social Recommendation With Topics And Brands, Minh Duc Luu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Consumer behavior and marketing research have shown that brand has significant influence on product reviews and product purchase decisions. However, there is very little work on incorporating brand related factors into product recommender systems. Meanwhile, the similarity in brand preference between a user and other socially connected users also affects her adoption decisions. To integrate seamlessly the individual and social brand related factors into the recommendation process, we propose a novel model called Social Brand–Item–Topic (SocBIT). As the original SocBIT model does not enforce non-negativity, which poses some difficulty in result interpretation, we also propose a non-negative version, called SocBIT(Formula …

The Pharmacogene Variation (Pharmvar) Consortium: Incorporation Of The Human Cytochrome P450 (Cyp) Allele Nomenclature Database, Andrea Gaedigk, Magnus Ingelman-Sundberg, Neil A. Miller, J Steven Leeder, Michelle Whirl-Carrillo, Teri E. Klein

Manuscripts, Articles, Book Chapters and Other Papers

The Human Cytochrome P450 (CYP) Allele Nomenclature Database, a critical resource to the pharmacogenetics and genomics communities, will be transitioning to the Pharmacogene Variation (PharmVar) Consortium. In this report we provide a summary of the current database, provide an overview of the PharmVar consortium and highlight the PharmVar database which will serve as the new home for pharmacogene nomenclature.

A Holistic Methodology For Profiling Ransomware Through Endpoint Detection, Stefani K. Hobratsch

Masters Theses & Doctoral Dissertations

Computer security incident response is a critical capability in light of the growing threat of malware infecting endpoint systems today. Ransomware is one type of malware that is causing increasing harm to organizations. Ransomware infects an endpoint system by encrypting files until a ransom is paid. Ransomware can have a negative impact on an organization’s daily functions if critical business files are encrypted and are not backed up properly.

Many tools exist that claim to detect and respond to malware. Organizations and small businesses are often short-staffed and lack the technical expertise to properly configure security tools. One such endpoint …

An Efficient And Expressive Ciphertext-Policy Attribute-Based Encryption Scheme With Partially Hidden Access Structures, Revisited, Hui Cui, Robert H. Deng, Junzuo Lai, Xun Yi, Surya Nepal

Research Collection School Of Computing and Information Systems

Ciphertext-policy attribute-based encryption (CP-ABE) has been regarded as one of the promising solutions to protect data security and privacy in cloud storage services. In a CP-ABE scheme, an access structure is included in the ciphertext, which, however, may leak sensitive information about the underlying plaintext and the privileged recipients in that anyone who sees the ciphertext is able to learn the attributes of the privileged recipients from the associated access structure. In order to address this issue, CP-ABE with partially hidden access structures was introduced where each attribute is divided into an attribute name and an attribute value and the …

Obfuscation At-Source: Privacy In Context-Aware Mobile Crowd-Sourcing, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Randy Tandriansyah, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

By effectively reaching out to and engaging larger population of mobile users, mobile crowd-sourcing has become a strategy to perform large amount of urban tasks. The recent empirical studies have shown that compared to the pull-based approach, which expects the users to browse through the list of tasks to perform, the push-based approach that actively recommends tasks can greatly improve the overall system performance. As the efficiency of the push-based approach is achieved by incorporating worker's mobility traces, privacy is naturally a concern. In this paper, we propose a novel, 2-stage and user-controlled obfuscation technique that provides a trade off-amenable …

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, …