Computer Sciences Commons^™

A General Commonsense Explanation Of Several Medical Results, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that many recent experimental medical results about the effect of different factors on our health can be explained by common sense ideas.

Guest Editorial: Special Section On Distributed Intelligence Over Internet Of Things, Honglong Chen, Joel Rodrigues, Feng Xia, Sajal K. Das

Computer Science Faculty Research & Creative Works

No abstract provided.

Random Partition Based Adaptive Distributed Kernelized Svm For Big Data, Amrit Pal, Abishi Chowdhury, Satakshi, Husnu S. Narman, Arkabandhu Chowdhury, Manish Kumar

Computer Sciences and Electrical Engineering Faculty Research

In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster. While handling such large data, the existing approaches consider specific data partitioning techniques which demand complete data be processed before partitioning. This leads to an excessive overhead of high computation and data communication. The proposed method does not require any pre-structured data partitioning technique and is also adaptive to big data mining tools. We hypothesize that an effective aggregation of the information generated from data partitions by subprocesses of the complete learning process can lead …

Compressed Sensing Based Low-Power Multi-View Video Coding And Transmission In Wireless Multi-Path Multi-Hop Networks, Nan Cen, Zhangyu Guan, Tommaso Melodia

Computer Science Faculty Research & Creative Works

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and …

“Pictures Are Easier To Remember Than Spellings!”: Designing And Evaluating Kidspic: A Graphical Image-Based Authentication Mechanism, Dhanush Kumar Ratakonda, Hoda Mehrpouyan, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

Children encounter difficulties when they login to computers or websites because they have challenges remembering passwords. To improve children’s authentication, we conducted a series of formative studies with children (n = 8, ages 6–11) to understand their authentication practices with respect to a traditional text-based password and a new graphical picture-based password called KidsPic. The results obtained from these initial investigations, a security analysis of these authentication mechanisms, and participatory design sessions with children (ages 6–11) inspired design enhancements to KidsPic. We subsequently conducted a study comparing KidsPic to a traditional text-based authentication mechanism (n = …

Pushing Boundaries Of Co-Design By Going Online: Lessons Learned And Reflections From Three Perspectives, Jerry Alan Fails, Dhanush Kumar Ratakonda, Nitzan Koren, Salma Elsayed-Ali, Elizabeth Bonsignore, Jason Yip

Computer Science Faculty Publications and Presentations

The global COVID-19 pandemic made significant changes to our day-to-day lives, which impacted how we conduct research and design — including co-design. In this article, we present case studies from three different co-design groups that pushed the boundaries of traditional co-design, and conducted multiple co-design sessions (more than 150 total) over the last year and a half. The case studies for each team include: the transition to online co-design; the pros and cons of logistics and design tools utilized during the co-design sessions; and the advances, challenges, and surprises. We compare and contrast themes that emerged from the case studies …

Why Best-Worst Method Works Well, Sean Aguilar, Vladik Kreinovich

Departmental Technical Reports (CS)

In many cases, experts are much more accurate when they estimate the ratio of two quantities than when they estimate the actual values. For example, if it difficult to accurately estimate the height of a person on a photo, but if we have two people standing side by side, we can easily estimate to what extent one of them is taller than the other one. To get accurate estimates, it is therefore desirable to use such ratio estimates. Empirical analysis shows that to obtain the most accurate results, we need to compare all the objects with either the "best" object …

How Hot Is Too Hot, Sofia Holguin, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent study has shown that the temperature threshold -- after which even young healthy individuals start feeling the effect of heat on their productivity -- is 30.5 ± 1 C. In this paper, we use decision theory ideas to provide a theoretical explanation for this empirical finding.

Invariance Explains Empirical Success Of Many Intelligent Techniques, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many applications of intelligent computing, we need to choose an appropriate function -- e.g., an appropriate re-scaling function, or an appropriate aggregation function. In applications of intelligent techniques, the problem of selecting an optimal function is usually too complex or too imprecise to be solved analytically, so the best functions are found empirically, by trying a large number of alternatives. In this paper, we show that in many such cases, the resulting empirical choice can be explained by natural invariance ideas. Example range from applications to building blocks of intelligent techniques -- such as aggregation (including hierarchical aggregation) and …

Why Exponential Almon Lag Works Well In Econometrics: An Invariance-Based Explanation, Laxman Bokati, Vladik Kreinovich

Departmental Technical Reports (CS)

In many econometric situations, we can predict future values of relevant quantities by using an empirical formula known as exponential Almon lag. While this formula is empirically successful, there have been no convincing theoretical explanation for this success. In this paper, we provide such a theoretical explanation based on general invariance ideas.

Seemingly Counter-Intuitive Features Of Good-To-Great Companies Actually Make Perfect Sense: Possible Algorithmics-Based Explanations, Francisco Zapata, Eric Smith, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the late 1990s, researchers analyzed what distinguishes great companies from simply good ones. They found several features that are typical for great companies. Interestingly, most of these features seem counter-intuitive. In this paper, we show that from the algorithmic viewpoint, many of these features make perfect sense. Some of the resulting explanations are simple and straightforward, other explanations rely on complex not-well-publicized results from theoretical computer science.

Motion-Adjustable Neural Implicit Video Representation, Long Mai, Feng Liu

Computer Science Faculty Publications and Presentations

Implicit neural representation (INR) has been successful in representing static images. Contemporary image-based INR, with the use of Fourier-based positional encoding, can be viewed as a mapping from sinusoidal patterns with different frequencies to image content. Inspired by that view, we hypothesize that it is possible to generate temporally varying content with a single image-based INR model by displacing its input sinusoidal patterns over time. By exploiting the relation between the phase information in sinusoidal functions and their displacements, we incorporate into the conventional image-based INR model a phase-varying positional encoding module, and couple it with a phase-shift generation module …

Be Smart, Save I/O: A Probabilistic Approach To Avoid Uncorrectable Errors In Storage Systems, Md Arifuzzaman, Masudul Bhuiyan, Mehmet Gumus, Engin Arslan

Computer Science Faculty Research & Creative Works

Silent data corruption poses a significant risk to the integrity of data in storage systems. Although error correction codes (ECC) can recover the majority of such errors, a nonnegligible portion of them escape ECC, referred as uncorrectable errors (UEs). Despite being rare in nature, increasing scale of storage systems and fast-growing I/O rates decreased the mean time between UEs from months to hours. Yet, unlike disk failures, UEs are hard to predict with high precision, making it difficult to adopt proactive measures. In this paper, we introduce a probabilistic approach to deploy UE mitigation strategies that can capture significant portion …

Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?, Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li

Research Collection School Of Computing and Information Systems

Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory’s [25] hierarchical generative process and non-semantic factors by …

Singapore Public Sector Ai Applications Emphasizing Public Engagement: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

This article provides an overview of six examples of public sector AI applications in Singapore that illustrate different ways of enhancing engagement with the public. These applications demonstrate ways of enhancing engagement with the public by providing greater accessibility to government services (access anywhere, anytime) and speedier responses to public processes and feedback. Some applications make it substantially easier for members of the public to do things or make choices, while others reduce waiting time, either across an entire public infrastructure, or for an individual transaction. Some provide highly individualized coaching to guide a person through the process of doing …

Secure Hierarchical Deterministic Wallet Supporting Stealth Address, Xin Yin, Zhen Liu, Guomin Yang, Guoxing Chen, Haojin Zhu

Research Collection School Of Computing and Information Systems

Over the past decade, cryptocurrency has been undergoing a rapid development. Digital wallet, as the tool to store and manage the cryptographic keys, is the primary entrance for the public to access cryptocurrency assets. Hierarchical Deterministic Wallet (HDW), proposed in Bitcoin Improvement Proposal 32 (BIP32), has attracted much attention and been widely used in the community, due to its virtues such as easy backup/recovery, convenient cold-address management, and supporting trust-less audits and applications in hierarchical organizations. While HDW allows the wallet owner to generate and manage his keys conveniently, Stealth Address (SA) allows a payer to generate fresh address (i.e., …

Products Pricing And Return Strategies For The Dual Channel Retailers, Jian Liu, Xinyue Sun, Yanyan Liu

Electrical and Computer Engineering Faculty Research & Creative Works

This paper analyzed how different return strategies and return rates affect dual-channel retailers' profits and channel pricings. Return can stimulate sales; however, the return has presented significant challenges to retailers. The return has long been studied to maximize profit and pricing; however, the different return strategies for dual-channel retailers affect channel both. This paper aimed to study whether or not dual-channel retailers should allow customers to return items in two channels and whether or not the retailer should contract with the manufacturers and pay extra fees to return products. This study indicated when the retailer should allow customers' returns to …

Algorithm-Based Fault Tolerance At Scale, Hayden Estes

Summer Community of Scholars Posters (RCEU and HCR Combined Programs)

No abstract provided.

Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo

Theses and Dissertations

Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a …

Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero

Theses and Dissertations

Quantum computers are beset by errors from a variety of sources. Although quantum error correction and detection codes have been developed since the 1990s, these codes require mid-circuit measurements in order to operate. In order to avoid these measurements we have developed a new error detection code that only requires state collapses at the end of the circuit, which we call no ancilla error detection (NAED). We investigate some of the mathematics behind NAED such as which codes can detect which errors. We then run NAED on three separate types of circuits: Greenberger–Horne–Zeilinger circuits, phase dependent circuits, and a quantum …

Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho

Theses and Dissertations

We develop new machine learning and statistical methods that are tailored for Air and Space applications through the incorporation of subject matter expertise. In particular, we focus on three separate research thrusts that each represents a different type of subject matter knowledge, modeling approach, and application. In our first thrust, we incorporate knowledge of natural phenomena to design a neural network algorithm for localizing point defects in transmission electron microscopy (TEM) images of crystalline materials. In our second research thrust, we use Bayesian feature selection and regression to analyze the relationship between fighter pilot attributes and flight mishap rates. We …

Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug

Theses and Dissertations

Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …

A Conical-Beam Dual-Band Double Aperture-Coupled Stacked Elliptical Patch Antenna Design For 5g, Feza Turgay Çeli̇k, Kami̇l Karaçuha

Turkish Journal of Electrical Engineering and Computer Sciences

This study investigates a dual-band, aperture coupled and stacked elliptical patch antenna with conical radiation. To obtain such characteristics, the TM21 mode of the radiating elliptical patches is excited by utilizing two special apertures and shorting planes. The proposed antenna operates both at 2.45 GHz and 3.5 GHz. The design aims to be used indoor applications of 5G operating in both free and planned 5G bands, respectively. Therefore, the low-profile antenna element that has a monopole-like radiation pattern is a good candidate for two-dimensional arraying. The design steps and the evolution of the proposed antenna are presented in detail. The …

Segmentation Of Diatoms Using Edge Detection And Deep Learning, Hüseyi̇n Gündüz, Cüneyd Nadi̇r Solak, Serkan Günal

Turkish Journal of Electrical Engineering and Computer Sciences

Diatoms are photosynthesizing algae found in almost every aquatic environment. Detecting the number and diversity of diatoms is very important to analyze water quality appropriately. Accurate segmentation of diatoms is therefore crucial for this detection process. In this study, a new and effective model for the automatic segmentation of diatoms based on image processing and deep learning algorithms is proposed. In the proposed model, edge segments of a given image containing diatoms and nondiatom particles are first obtained. These edge segments are then combined, resulting in closed contours representing diatom candidates. In the final step, the diatom candidates are classified …

Degrees Of Confidence As A Legal Tool To Assess Ai System Liability, Joshua Song

Michigan Technology Law Review

AI systems have become increasingly integrated into our everyday lives, and harms caused by these systems have graduated from raising hypothetical ethical concerns to questions of actual legal liability. Civil liability schemes are generally designed to address harms caused by humans; thus, it may be tempting to analogize new types of harms caused by AI systems to familiar harms caused by humans in order to justify commandeering existing human-centered legal tools to assess AI liability. However, the analogy is inappropriate and misrepresents salient legal differences in how harms are committed by humans and AI systems. Thus, “as is often the …

Design And Optimization Of Nanooptical Couplers Based On Photonic Crystals Involving Dielectric Rods Of Varying Lengths, Şi̇ri̇n Yazar, Özgür Sali̇h Ergül

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents design and optimization of compact and efficient nanooptical couplers involving photonic crystals. Nanooptical couplers that have single and double input ports are designed to obtain efficient transmission of electromagnetic waves in desired directions. In addition, these nanooptical couplers are cascaded by adding one after another to realize electromagnetic transmission systems. In the design and optimization of all these nanooptical couplers, the multilevel fast multipole algorithm, which is an efficient full-wave solution method, is used to perform electromagnetic analyses and simulations. A heuristic optimization method based on genetic algorithms is employed to obtain effective designs that provide the …

Chainscan: A Blockchain-Based Supply Chain Alerting Framework For Food Safety, Jorge Castillo, Kevin Barba, Qian Chen

Informatics and Engineering Systems Faculty Publications

Supply Chain Management (SCM) systems provide a digital platform for the supply chain organizations to communicate and exchange information. In the food industry, SCMs provide the essential software components for monitoring food transportation and product handling. However, contemporary SCM systems suffer from non-traceability, non-interoperability and poor resiliency. Global food supply chains are at risks from cyber threats, which will lead to food shortages and poor quality control. In this paper, we present ChainSCAN, a three-layer design of a fully decentralized and blockchain-based SCM system that protects data security, privacy, and remove single point of failures of traditional SCMs. In addition, …

Dark Web Analytics : A Comparative Study Of Feature Selection And Prediction Algorithms, Andrew Allhusen, Izzat Alsmadi, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Ahmad Al-Omari

Computer Information Systems Faculty Publications (Archived)

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learning methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level data coming from the dark web. Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’s accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved …

Forensic Investigation Of Google Meet For Memory And Browser Artifacts, Farkhund Iqbal, Zainab Khalid, Andrew Marrington, Babar Shah, Patrick C.K. Hung

All Works

Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication …

Implementing Github Actions Continuous Integration To Reduce Error Rates In Ecological Data Collection, Albert Y. Kim, Valentine Herrmann, Ross Barreto, Brianna Calkins, Erika Gonzalez-Akre, Daniel J. Johnson, Jennifer A. Jordan, Lukas Magee, Ian R. Mcgregor, Nicolle Montero, Karl Novak, Teagan Rogers, Jessica Shue, Kristina J. Anderson-Teixeira

Statistical and Data Sciences: Faculty Publications

Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection.

Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, …