Digital Commons Network^™

A Survey On Security Analysis Of Machine Learning-Oriented Hardware And Software Intellectual Property, Ashraful Tauhid, Lei Xu, Mostafizur Rahman, Emmett Tomai

Computer Science Faculty Publications and Presentations

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software …

Differences In Intracellular Protein Levels In Monocytes And Cd4+ Lymphocytes Between Bipolar Depressed Patients And Healthy Controls: A Pilot Study With Tyramine-Based Signal-Amplified Flow Cytometry, Keming Gao, Marzieh Ayati, Nicholas M. Kaye, Mehmet Koyutürk, Joseph R. Calabrese, Stephen J. Ganocy, Hillard M. Lazarus, Eric Christian, David Kaplan

Computer Science Faculty Publications and Presentations

Highlights

• To measure 18 intracellular proteins in blood cells of bipolar depressed patients and healthy controls;

• TFour proteins in monocytes and 2 proteins in CD4+ T Cells were significantly lower in patients than in healthy controls;

• The studied proteins are involved in prolactin, leptin, BDNF, and interleukin-3 signal pathways;

• Studying intracellular proteins with enhanced flow cytometry may find biomarkers differentiating bipolar disorder from healthy controls.

Abstract

Background

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for …

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

Caspi: Collaborative Photon Processing For Active Single-Photon Imaging, Jongho Lee, Atul Ingle, Jenu V. Chacko, Kevin W. Eliceiri, Mohit Gupta

Computer Science Faculty Publications and Presentations

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a …

In-Vitro Validated Methods For Encoding Digital Data In Deoxyribonucleic Acid (Dna), Golam Md Mortuza, Jorge Guerrero, Shoshanna Llewellyn, Michael D. Tobiason, George D. Dickinson, William L. Hughes, Reza Zadegan, Tim Andersen

Computer Science Faculty Publications and Presentations

Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments.

Security Attacks And Countermeasures In Smart Homes, Hasibul Alam, Emmett Tomai

Computer Science Faculty Publications and Presentations

The Internet of Things (IoT) application is visible in all aspects of humans’ day-to-day affairs. The demand for IoT is growing at an unprecedented rate, from wearable wristwatches to autopilot cars. The smart home has also seen significant advancements to improve the quality of lifestyle. However, the security and privacy of IoT devices have become primary concerns as data is shared among intelligent devices and over the internet in a smart home network. There are several attacks - node capturing attack, sniffing attack, malware attack, boot phase attack, etc., which are conducted by adversaries to breach the security of smart …

Divergent Directionality Of Immune Cell-Specific Protein Expression Between Bipolar Lithium Responders And Non-Responders Revealed By Enhanced Flow Cytometry, Keming Gao, Nicholas M. Kaye, Marzieh Ayati, Mehmet Koyuturk, Joseph R. Calabrese, Eric Christian, Hillard M. Lazarus, David Kaplan

Computer Science Faculty Publications and Presentations

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4+ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4+ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow …

Convolution Neural Networks For Phishing Detection, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Phishing is one of the significant threats in cyber security. Phishing is a form of social engineering that uses e-mails with malicious websites to solicitate personal information. Phishing e-mails are growing in alarming number. In this paper we propose a novel machine learning approach to classify phishing websites using Convolution Neural Networks (CNNs) that use URL based features. CNNs consist of a stack of convolution, pooling layers, and a fully connected layer. CNNs accept images as input and perform feature extraction and classification. Many CNN models are available today. To avoid vanishing gradient problem, recent CNNs use entropy loss function …

Multispectral Image Analysis Using Convolution Neural Networks, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Machine learning (ML) techniques are used often to classify pixels in multispectral images. Recently, there is growing interest in using Convolution Neural Networks (CNNs) for classifying multispectral images. CNNs are preferred because of high performance, advances in hardware such as graphical processing units (GPUs), and availability of several CNN architectures. In CNN, units in the first hidden layer view only a small image window and learn low level features. Deeper layers learn more expressive features by combining low level features. In this paper, we propose a novel approach to classify pixels in a multispectral image using deep convolution neural networks …

Tiny Language Models Enriched With Multimodal Knowledge From Multiplex Networks, Clayton Fields, Osama Natouf, Andrew Mcmains, Catherine Henry, Casey Kennington

Computer Science Faculty Publications and Presentations

Large transformer language models trained exclusively on massive quantities of text are now the standard in NLP. In addition to the impractical amounts of data used to train them, they require enormous computational resources for training. Furthermore, they lack the rich array of sensory information available to humans, who can learn language with much less exposure to language. In this study, performed for submission in the BabyLM challenge, we show that we can improve a small transformer model’s data efficiency by enriching its embeddings by swapping the learned word embeddings from a tiny transformer model with vectors extracted from a …

Exploring Transformers As Compact, Data-Efficient Language Models, Clayton Fields, Casey Kennington

Computer Science Faculty Publications and Presentations

Large scale transformer models, trained with massive datasets have become the standard in natural language processing. The huge size of most transformers make research with these models impossible for those with limited computational resources. Additionally, the enormous pretraining data requirements of transformers exclude pretraining them with many smaller datasets that might provide enlightening results. In this study, we show that transformers can be significantly reduced in size, with as few as 5.7 million parameters, and still retain most of their downstream capability. Further we show that transformer models can retain comparable results when trained on human-scale datasets, as few as …

Ethics Of Emerging Communication And Collaboration Technologies For Children, Juan Pablo Hourcade, Elizabeth Bonsignore, Tamara Clegg, Flannery Currin, Jerry A. Fails, Georgie Qiao Jin, Summer R. Schmuecker, Lana Yarosh

Computer Science Faculty Publications and Presentations

This SIG will provide child-computer interaction researchers and practitioners, as well as other interested CSCW attendees, an opportunity to discuss topics related to the ethics of emerging communication and collaboration technologies for children. The child-computer interaction community has conducted many discussions on ethical issues, including a recent SIG at CHI 2023. However, the angle of communication and collaboration has not been a focus, even though emerging technologies could affect these aspects in significant ways. Hence, there is a need to consider emerging technologies, such as extended reality, and how they may impact the way children communicate and collaborate in face-to-face, …

Rdkg: A Reinforcement Learning Framework For Disease Diagnosis On Knowledge Graph, Shipeng Guo, Kunpeng Liu, Pengfei Wang, Weiwei Dai, Yi Du, Yuanchun Zhou, Wenjuan Cui

Computer Science Faculty Publications and Presentations

Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method …

Exploring Spectral Bias In Time Series Long Sequence Forecasting, Kofi Nketia Ackaah-Gyasi, Sergio Valdez, Yifeng Gao, Li Zhang

Computer Science Faculty Publications and Presentations

Transformers have achieved great success in the task of time series long sequence forecasting (TLSF) in recent years. However, existing research has pointed out that over-parameterized deep learning models are in favor of low frequency and could be difficult to capture high-frequency information for regression fitting task, named spectral bias. Yet the effect of such bias on TLSF problem, an auto-regressive problem with a long forecasting length, has not been explored. In this work, we take the first step to investigate the spectral bias issues in TLSF task for state-of-the-art models. Specifically, we carefully examine three different existing time series …

Sublinear Approximation Schemes For Scheduling Precedence Graphs Of Bounded Depth, Bin Fu, Yumei Huo, Hairong Zhao

Computer Science Faculty Publications and Presentations

We study the classical scheduling problem on parallel machines %with precedence constraints where the precedence graph has the bounded depth h. Our goal is to minimize the maximum completion time. We focus on developing approximation algorithms that use only sublinear space or sublinear time. We develop the first one-pass streaming approximation schemes using sublinear space when all jobs' processing times differ no more than a constant factor c and the number of machines m is at most 2nϵ3hc. This is so far the best approximation we can have in terms of m, since no polynomial time approximation better than 43 …

Pmp: Privacy-Aware Matrix Profile Against Sensitive Pattern Inference, Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin

Computer Science Faculty Publications and Presentations

Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner. The high resolution of TS brings new challenges in protecting privacy. While meaningful information in high-resolution TS shifts from concrete point values to local shape-based segments, numerous research have found that long shape-based patterns could contain more sensitive information and may potentially …

Covert Computation In The Abstract Tile-Assembly Model, Robert M. Alaniz, Timothy Gomez, Andrew Rodriguez, Tim Wylie, David Caballero, Elize Grizzell, Robert Schweller

Computer Science Faculty Publications and Presentations

There have been many advances in molecular computation that offer benefits such as targeted drug delivery, nanoscale mapping, and improved classification of nanoscale organisms. This power led to recent work exploring privacy in the computation, specifically, covert computation in self-assembling circuits. Here, we prove several important results related to the concept of a hidden computation in the most well-known model of self-assembly, the Abstract Tile-Assembly Model (aTAM). We show that in 2D, surprisingly, the model is capable of covert computation, but only with an exponentialsized assembly. We also show that the model is capable of covert computation with polynomial-sized assemblies …

Prediction Of Kinase-Substrate Associations Using The Functional Landscape Of Kinases And Phosphorylation Sites, Marzieh Ayati, Serhan Yılmaz, Filipa Blasco Tavares Pereira Lopes, Mark R. Chance, Mehmet Koyutürk

Computer Science Faculty Publications and Presentations

Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships …

Adaptive Resolution Loss: An Efficient And Effective Loss For Time Series Self-Supervised Learning Framework, Kevin Garcia, Juan Manuel Perez, Yifeng Gao

Computer Science Faculty Publications and Presentations

Time series data is a crucial form of information that has vast opportunities. With the widespread use of sensor networks, largescale time series data has become ubiquitous. One of the most prominent problems in time series data mining is representation learning. Recently, with the introduction of self-supervised learning frameworks (SSL), numerous amounts of research have focused on designing an effective SSL for time series data. One of the current state-of-the-art SSL frameworks in time series is called TS2Vec. TS2Vec specially designs a hierarchical contrastive learning framework that uses loss-based training, which performs outstandingly against benchmark testing. However, the computational cost …

Pmp: Privacy-Aware Matrix Profile Against Sensitive Pattern Inference For Time Series, Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin

Computer Science Faculty Publications and Presentations

Recent rapid development of sensor technology has allowed massive time series data to be collected and set foundation for the development of data-driven services and applications. During the process, data sharing is often required to allow modelers to perform specific time series data mining tasks based on the need of data owner. The high resolution of time series data brings new challenges in privacy protection, as meaningful information in high-resolution data shifts from concrete point values to shape-based patterns. Numerous research efforts have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused …

Adaptive Multiple Distributed Bidirectional Spiral Path Planning For Foraging Robot Swarms, Qi Lu, Ryan Luna

Computer Science Faculty Publications and Presentations

The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and …

Intellibeehive: An Automated Honey Bee, Pollen, And Varroa Destructor Monitoring System, Christian I. Narcia-Macias, Joselito Guardado, Jocell Rodriguez, Joanne Rampersad, Erik Enriquez, Dong-Chul Kim

Computer Science Faculty Publications and Presentations

Utilizing computer vision and the latest technological advancements, in this study, we developed a honey bee monitoring system that aims to enhance our understanding of Colony Collapse Disorder, honey bee behavior, population decline, and overall hive health. The system is positioned at the hive entrance providing real-time data, enabling beekeepers to closely monitor the hive's activity and health through an account-based website. Using machine learning, our monitoring system can accurately track honey bees, monitor pollen-gathering activity, and detect Varroa mites, all without causing any disruption to the honey bees. Moreover, we have ensured that the development of this monitoring system …

An Improved Lower Bound For Sparse Reconstruction From Subsampled Walsh Matrices, Jaroslaw Blasiok, Patrick Lopatto, Kyle Luh, Jake Marcinek, Shravas Rao

Computer Science Faculty Publications and Presentations

We give a short argument that yields a new lower bound on the number of uniformly and independently subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly and independently subsampling rows of an N ×N Walsh matrix contains a K-sparse vector in the kernel, unless the number of subsampled rows is Ω(KlogKlog(N/K)) — our lower bound applies whenever min(K,N/K) > logC N. Containing a sparse vector in the kernel precludes not only the restricted isometry property, but more generally the application of those matrices for …

An Equivalence Checking Framework For Agile Hardware Design, Yanzhao Wang, Fei Xie, Zhenkun Yang, Pascuale Cocchini, Jin Yang

Computer Science Faculty Publications and Presentations

Agile hardware design enables designers to produce new design iterations efficiently. Equivalence checking is critical in ensuring that a new design iteration conforms to its specification. In this paper, we introduce an equivalence checking framework for hardware designs represented in HalideIR. HalideIR is a popular intermediate representation in software domains such as deep learning and image processing, and it is increasingly utilized in agile hardware design.We have developed a fully automatic equivalence checking workflow seamlessly integrated with HalideIR and several optimizations that leverage the incremental nature of agile hardware design to scale equivalence checking. Evaluations of two deep learning accelerator …

The Role Of Preprocessing For Word Representation Learning In Affective Tasks, Nastaran Babanejad, Heidar Davoudi, Ameeta Agrawal, Manos Papagelis

Computer Science Faculty Publications and Presentations

Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection have drawn a lot of attention in recent years due to a broad range of useful applications in various domains. The main goal of affect detection tasks is to recognize states such as mood, sentiment, and emotions from textual data (e.g., news articles or product reviews). Despite the importance of utilizing preprocessing steps in different stages (i.e., word representation learning and building a classification model) of affect detection tasks, this topic has not been studied well. To that end, we explore whether applying various preprocessing methods (stemming, lemmatization, stopword removal, …

A Survey On Security Analysis Of Amazon Echo Devices, Surendra Pathak, Sheikh Ariful Islam, Honglu Jiang, Lei Xu, Emmett Tomai

Computer Science Faculty Publications and Presentations

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is used for diverse tasks such as accessing online information, making phone calls, purchasing items, and controlling the smart home. Echo offers user-friendly voice interaction to automate everyday tasks making it a massive success. Though many people view Amazon Echo as a helpful assistant at home or office, few know its underlying security and privacy implications. In this paper, we present the findings of our research on Amazon Echo’s security …

Reproducibility In Human-Robot Interaction: Furthering The Science Of Hri, Hatice Gunes, Frank Broz, Chris S. Crawford, Astrid Rosenthal-Von Der Putten, Megan K. Strait, Laurel Riek

Computer Science Faculty Publications and Presentations

Purpose of Review

To discuss the current state of reproducibility of research in human-robot interaction (HRI), challenges specific to the field, and recommendations for how the community can support reproducibility.

Recent Findings

As in related fields such as artificial intelligence, robotics, and psychology, improving research reproducibility is key to the maturation of the body of scientific knowledge in the field of HRI. The ACM/IEEE International Conference on Human-Robot Interaction introduced a theme on Reproducibility of HRI to their technical program in 2020 to solicit papers presenting reproductions of prior research or artifacts supporting research reproducibility.

Summary

This review provides an …

Quantum Key-Length Extension, Joseph Jaeger, Fang Song, Stefano Tessaro

Computer Science Faculty Publications and Presentations

Should quantum computers become available, they will reduce the effective key length of basic secret-key primitives, such as blockciphers. To address this we will either need to use blockciphers with inherently longer keys or develop key-length extension techniques to amplify the security of a blockcipher to use longer keys.

We consider the latter approach and revisit the FX and double encryption constructions. Classically, FX was proven to be a secure key-length extension technique, while double encryption fails to be more secure than single encryption due to a meet-in-the-middle attack. In this work we provide positive results, with concrete and tight …

Robust Time Series Chain Discovery With Incremental Nearest Neighbors, Li Zhang, Yan Zhu, Yifeng Gao, Jessica Lin

Computer Science Faculty Publications and Presentations

Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of time series motifs to identify the continuous evolving patterns in time series data. Informally, a time series chain (TSC) is a temporally ordered set of time series subsequences, in which every subsequence is similar to the one that precedes it, but the last and the first can be arbitrarily dissimilar. TSCs are shown to be able to reveal latent continuous evolving trends in the time series, and identify precursors of unusual events in …

From Machine Learning To Deep Learning: A Comprehensive Study Of Alcohol And Drug Use Disorder, Banafsheh Rekabdar, David L. Albright, Haelim Jeong, Sameerah Talafha

Computer Science Faculty Publications and Presentations

This study aims to train and validate machine learning and deep learning models to identify patients with risky alcohol and drug misuse in a Screening, Brief Intervention, and Referral to Treatment (SBIRT) program. An observational cohort of 6978 adults was admitted in the western region of Alabama at three medical facilities between January and December of 2019. Data were cleaned and pre-processed using data imputation techniques and an augmented sampling data method. The primary analysis involved the multi-class classification of alcohol and drug misuse. Our study shows that accurate identification of alcohol and drug use screening instrument scores was best …