Digital Commons Network^™

The Causes Of Electrical Labor Shortages Within Central California, Madeline S. Brogno

Construction Management

Labor shortages within various trades in the construction industry have become a prominent issue throughout the world for decades. The negative effects due to lack of manpower affect construction personnel ranging from general contractors to manufacturers who are finding themselves struggling as many subcontractors are having to turn down work, which in turn affects general contractors timelines in projects- extending durations, and increasing costs. There has been general research done that has provided some insight, suggesting the root cause be attributed to the baby-boomer generation retiring, or a general lack of interest in the industry. This research paper on labor …

Privacy-Aware And Hardware-Based Accleration Authentication Scheme For Internet Of Drones, Tom Henson

Master's Theses

Drones are becoming increasingly present into today’s society through many different means such as outdoor sports, surveillance, delivery of goods etc. With such a rapid increase, a means of control and monitoring is needed as the drones become more interconnected and readily available. Thus, the idea of Internet of drones (IoD) is formed, an infrastructure in place to do those types of things. However, without an authentication system in place anyone could gain access or control to real time data to multiple drones within an area. This is a problem that I choose to tackle using a Field Programmable Gate …

Quantifying Nitrogen Fixation By Heterotrophic Bacteria In Sinking Marine Particles, Subhendu Chakraborty, Ken H. Andersen, André W. Visser, Keisuke Inomura, Michael J. Follows, Lasse Riemann

Graduate School of Oceanography Faculty Publications

Nitrogen (N 2) fixation by heterotrophic bacteria associated with sinking particles contributes to marine N cycling, but a mechanistic understanding of its regulation and significance are not available. Here we develop a mathematical model for unicellular heterotrophic bacteria growing on sinking marine particles. These bacteria can fix N 2 under suitable environmental conditions. We find that the interactive effects of polysaccharide and polypeptide concentrations, sinking speed of particles, and surrounding O 2 and NO3− concentrations determine the N 2 fixation rate inside particles. N 2 fixation inside sinking particles is mainly fueled by SO42− respiration rather than NO3− respiration. Our …

A Multigrid Homotopy Method For Computing Eigenfunctions Of Differential Operators With Two-Dimensional Heterogeneity, Chelsea Drum

Master's Theses

In this thesis, we develop an accurate algorithm for computing the smallest eigenvalues of the self-adjoint spatial differential operator for the two-dimensional heat equation with a piecewise constant coefficient and periodic boundary conditions. The piecewise constant coefficient is created by the discontinuity of the diffusivity coefficient across the interfaces between two or more heterogeneous materials. Our method involves the combination of the well-known Inverse Iteration with a multigrid homotopy.

A Systematic Review Of Parent Training Effects Children's Use Of Augmentative And Alternative Communication Systems, Mia Constransitch

Undergraduate Honors Theses

Augmentative and Alternative Communication (AAC) are communication systems used when someone cannot express their needs with naturally spoken speech. For children who use AAC, parent training is an essential part because it teaches the parents how to use the device and encourages their child to use the device. A systematic review was completed to explore the literature regarding parent training for parents whose children are AAC users, and how the training impacts the child's success using the AAC system. The results from this can be used to explore if parent training is effective and if there is a need for …

Challenges For Estimating The Global Prevalence Of Micronutrient Deficiencies And Related Disease Burden: A Case Study Of The Global Burden Of Disease Study, Sonja Y. Hess, Alexander C. Mclain Ph.D., Edward A. Frongillo Jr. Ph.D., Ashkan Afshin, Nicholas J. Kassebaum, Saskia Jm Osendarp, Reed Atkin, Rahul Rawat, Kenneth H. Brown

Faculty Publications

Information on the prevalence of micronutrient deficiencies is needed to determine related disease burden; underpin evidence-based advocacy; and design, deliver, and monitor safe, effective interventions. Assessing the global prevalence of deficiency requires a valid micronutrient status biomarker with an appropriate cutoff to define deficiency and relevant data from representative surveys across multiple locations and years. The Global Burden of Disease Study includes prevalence estimates for iodine, iron, zinc, and vitamin A deficiencies, for which recommended biomarkers and appropriate deficiency cutoffs exist. Because representative survey data are lacking, only retinol concentration is used to model vitamin A deficiency, and proxy indicators …

Spatiotemporal Pattern Detection With Neuromorphic Circuits, Robert C. Ivans

Boise State University Theses and Dissertations

In this dissertation, neuromorphic circuits are used to implement spiking neural networks in order to detect spatiotemporal patterns. Unsupervised training and detection-by-design techniques were used to attain the appropriate connectomes and perform pattern detection.

Unsupervised training was performed by feeding random digital spikes with a repeating embedded spatiotemporal pattern to a spiking neural network composed of leaky integrate-and-fire neurons and memristor-R(t) element circuits which implement spike-timing-dependent plasticity learning rules.

Detection-by-design was achieved using neuromporphic circuits and digital logic gates. When detection-by-design was achieved using both neuromorphic circuits and digital logic gates, a network was created of spatiotemporal pattern detector circuits, …

Developing Strategy For A Successful Antioxidant, Anticancer Activity Via An Improved Method Prepared To Porous Silicon Nanoparticles, Kareem Jawad, Butheina Hasson

Journal of Applied Sciences and Nanotechnology

Porous silicon nanoparticles, or PSNPs, are one of the most common NPS with distinct characteristics. PSNPs were created using an enhanced approach known as the electrochemical etching process. The nanoparticles were treated with a post-laser to obtain the nanoparticles, which were subsequently analyzed using (SEM), (UV-Vis), and (XRD). The size refers to porous silicon with a nanostructure. Because of the differences in interaction between the HF electrolyzed and silicon and crystal structure, PS (100) offers better NP properties than directional silicon (111). PSNPs' antioxidant activity was measured using the DPPH test, while cytotoxicity was measured using the MTT assay on …

A Generalization Of The Concave Integral In Terms Of Decomposition Of The Integrated Function For Bipolar Scales, Fadheela Kareem, Jabbar Abbas

Journal of Applied Sciences and Nanotechnology

In the context of the multiple-criteria decision aid (MCDA), several fuzzy integrals concerning capacities (non-additive measures) have been introduced by various researchers in the last sixty years.  Recently, Lehrer has proposed a new integral for capacities known as concave integral. The concave integral is based on the decomposition of random variables into simple ingredients. The concave integral concerning capacity is defined as the maximum value obtained among all its decompositions. The paper aims to model a new integration based on the decomposition of random variables into simple ingredients for multi-criteria decision making support when underlying scales are bipolar.  This paper …

Checking Smart Contracts With Structural Code Embedding, Zhipeng Gao, Lingxiao Jiang, Xin Xia, David Lo, John Grundy

Research Collection School Of Computing and Information Systems

Smart contracts have been increasingly used together with blockchains to automate financial and business transactions. However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this paper, we propose an automated approach to learn characteristics of smart contracts in Solidity, useful for repetitive contract code, bug detection and contract validation. Our new approach is based on …

Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …

Microservices Orchestration Vs. Choreography: A Decision Framework, Alan @ Ali Madjelisi Megargel, Christopher M. Poskitt, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Microservices-based applications consist of loosely coupled, independently deployable services that encapsulate units of functionality. To implement larger application processes, these microservices must communicate and collaborate. Typically, this follows one of two patterns: (1) choreography, in which communication is done via asynchronous message-passing; or (2) orchestration, in which a controller is used to synchronously manage the process flow. Choosing the right pattern requires the resolution of some trade-offs concerning coupling, chattiness, visibility, and design. To address this problem, we propose a decision framework for microservices collaboration patterns that helps solution architects to crystallize their goals, compare the key factors, and then …

Degree Doesn't Matter: Identifying The Drivers Of Interaction In Software Development Ecosystem, Amrita Bhattacharjee, Subhajit Datta, Subhashis Majumder

Research Collection School Of Computing and Information Systems

Large scale software development ecosystems represent one of the most complex human enterprises. In such settings, developers are embedded in a web of shared concerns, responsibilities, and objectives at individual and collective levels. A deep understanding of the factors that influence developers to connect with one another is crucial in appreciating the challenges of such ecosystems as well as formulating strategies to overcome those challenges. We use real world data from multiple software development ecosystems to construct developer interaction networks and examine the mechanisms of such network formation using statistical models to identify developer attributes that have maximal influence on …

Integration Of Information Technology Certifications Into Undergraduate Computing Curriculum, Eng Lieh Ouh, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

This innovative practice full paper describes our experiences of integrating information technology certifications into an undergraduate computing curriculum. As the technology landscape evolves, a common challenge for educators in computing programs is designing an industry-relevant curriculum. Over the years, industry practitioners have taken technology certifications to validate themselves against a base level of technical knowledge currently in demand in industry. Information technology (IT) certifications can also offer paths for academic computing programs to stay relevant to industry needs. However, identifying relevant IT certifications and integrating it into an academic curriculum requires a careful design approach as substantial efforts are needed …

Adadeep: A Usage-Driven, Automated Deep Model Compression Framework For Enabling Ubiquitous Intelligent Mobiles, Sicong Liu, Junzhao Du, Kaiming Nan, Zimu Zhou, Hui Liu, Zhangyang Wang, Yingyan Lin

Research Collection School Of Computing and Information Systems

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this …

Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.

Solving The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers By Simulated Annealing, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-Chi Huang

Research Collection School Of Computing and Information Systems

This research studies the vehicle routing problem with simultaneous pickup and delivery with an occasional driver (VRPSPDOD). VRPSPDOD is a new variant of the vehicle routing problems with simultaneous pickup and delivery (VRPSPD). Different from VRPSPD, in VRPSPDOD, occasional drivers are employed to work with regular vehicles to service customers’ pickup and delivery requests in order to minimize the total cost. We formulate a mixed integer linear programming model for VRPSPD and propose a heuristic algorithm based on simulated annealing (SA) to solve the problem. The results of comprehensive numerical experiments show that the proposed SA performs well in terms …

Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …

Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-Incremental Learning (CIL) [38] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the …

Spurring Digital Transformation In Singapore's Legal Industry, Xin Juan Chua, Steven M. Miller

Research Collection School Of Computing and Information Systems

COVID-19 has transformed the way we live and work. It has caused the processes and operations of businesses and organisations to be restructured, as well as transformed business models. A 2020 McKinsey Global survey reported that companies all over the world claim they have accelerated the digitalisation of their customer and supply-chain interactions, as well as their internal operations, by three to four years. They also said they thought the share of digital or digitally enabled products in their portfolios has advanced by seven years. While technology transformation is not new to the legal profession, COVID-19 has cemented the importance …

Statistical Moderation: A Case Study In Grading On A Curve, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

There is a negative perception about “grading on a curve,” because of the feeling that the cohort strength may skew the final grades one way or another. However, given the difficulties in ensuring absolute uniformity in assessment across the years, especially when taught and assessed by different instructors under different settings, grading on a curve may be a necessary evil. Once we accept this type of statistical moderation as the last line of defense in standardizing the final scores so that student cohorts from different terms or sections or schools may be compared, we have to implement it well. In …

Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

Online hate speech has disrupted the social connectedness in online communities and raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many machine learning and deep learning methods to detect hate speech in social media automatically. However, most of the existing automated solutions have focused on detecting hate speech in a single post, neglecting the network and information propagation effects of social media platforms. Ideally, the content moderators would want to identify the hateful posts and monitor posts and threads that are likely to induce hate. This paper aims to address this research gap …

Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng

Research Collection School Of Computing and Information Systems

A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.

Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft

Research Collection School Of Computing and Information Systems

In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of \widetilde{O}(rd2) to \widetilde{O}(d3/2√r), where d is the dimension of the side information and rr is the rank. (2) We introduce the (smoothed) \textit{adjusted trace-norm minimization} strategy, an inductive analogue of the weighted trace norm, for which we show guarantees of the order \widetilde{O}(dr) under arbitrary sampling. In the inductive case, a similar rate was previously achieved only under uniform sampling …

Functional Signatures: New Definition And Constructions, Qingwen Guo, Qiong Huang, Sha Ma, Meiyan Xiao, Guomin Yang, Willy Susilo

Research Collection School Of Computing and Information Systems

Functional signatures (FS) enable a master authority to delegate its signing privilege to an assistant. Concretely, the master authority uses its secret key sk(F) to issue a signing key sk(f) for a designated function f is an element of F-FS and sends both f and sk(f) to the assistant E, which is then able to compute a signature sigma(f) with respect to pk(F) for a message y in the range of f. In this paper, we modify the syntax of FS slightly to support the application scenario where a certificate of authorization is necessary. Compared with the original FS, our …

Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang

Research Collection School Of Computing and Information Systems

The emergence of public-key encryption with keyword search (PEKS) has provided an elegant approach to enable keyword search over encrypted content. Due to its high computational complexity proportional to the number of intended receivers, the trivial way of deploying PEKS for data sharing with multiple receivers is impractical, which motivates the development of a new PEKS framework for broadcast mode. However, existing works suffer from either the vulnerability to keyword guessing attacks (KGA) or high computation and communication complexity. In this work, a new primitive for keyword search in broadcast mode, named broadcast authenticated encryption with keyword search (BAEKS), is …

Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He

Research Collection School Of Computing and Information Systems

Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image. To this end, we propose two cascaded networks, the motion embedding network and the motion expansion network. The motion embedding network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the motion expansion network is proposed to …

Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network’s prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior’s support. …

Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this …

Quantifying Planarian Behavior As An Introduction To Object Tracking And Signal Processing, Nicole C. Stowell, T. Goel, Vir Shetty , '22, Jocelyne Noveral, Eva-Maria S. Collins

Biology Faculty Works

Answers to mechanistic questions about biological phenomena require fluency in a variety of molecular biology techniques and physical concepts. Here, we present an interdisciplinary approach to introducing undergraduate students to an important problem in the areas of animal behavior and neuroscience—the neuronal control of animal behavior. In this lab module, students explore planarian behavior by quantitative image and data analysis with freely available software and low-cost resources. Planarians are ∼1–2-cm-long aquatic free-living flatworms famous for their regeneration abilities. They are inexpensive and easy to maintain, handle, and perturb, and their fairly large size allows for image acquisition with a webcam, …