Computer Sciences Commons^™

Mobile Health With Head-Worn Devices: Challenges And Opportunities, Andrea Ferlini, Dong Ma, Lorena Qendro, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Monitoring human behavior and health status using mobile devices, a.k.a. Mobile Health, has gained increasing attention from both academia and industry in recent years. It allows imperceptible health tracking from the users and remote health management from the healthcare service providers. Headworn devices, such as earbuds, glasses, and BCIs (Brain Computer Interfaces), exhibit great potential for mobile health due to their advantageous wearing position, the human head, which is motion-resilient and full of human bio-signals. Although initial attempts have been conducted for different healthcare applications with head-worn devices, this fast-growing area is still under-explored and retains great promises. With this …

Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention …

Constraint-Aware, Scalable, And Efficient Algorithms For Multi-Chip Power Module Layout Optimization, Imam Al Razi

Graduate Theses and Dissertations

Moving towards an electrified world requires ultra high-density power converters. Electric vehicles, electrified aerospace, data centers, etc. are just a few fields among wide application areas of power electronic systems, where high-density power converters are essential. As a critical part of these power converters, power semiconductor modules and their layout optimization has been identified as a crucial step in achieving the maximum performance and density for wide bandgap technologies (i.e., GaN and SiC). New packaging technologies are also introduced to produce reliable and efficient multichip power module (MCPM) designs to push the current limits. The complexity of the emerging MCPM …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Holistic Combination Of Structural And Textual Code Information For Context Based Api Recommendation, Chi Chen, Xin Peng, Zhengchang Xing, Jun Sun, Xin Wang, Yifan Zhao, Wenyun Zhao

Research Collection School Of Computing and Information Systems

Context based API recommendation is an important way to help developers find the needed APIs effectively and efficiently. For effective API recommendation, we need not only a joint view of both structural and textual code information, but also a holistic view of correlated API usage in control and data flow graph as a whole. Unfortunately, existing API recommendation methods exploit structural or textual code information separately. In this work, we propose a novel API recommendation approach called APIRec-CST (API Recommendation by Combining Structural and Textual code information). APIRec-CST is a deep learning model that combines the API usage with the …

Ransomsoc: A More Effective Security Operations Center To Detect And Respond To Ransomware Attacks, Anthony Cheuk Tung Lai, Ping Fan Ke, Kelvin Chan, Siu Ming Yiu, Dongsun Kim, Wai Kin Wong, Shuai Wang, Joseph Muppala, Alan Ho

Research Collection School Of Computing and Information Systems

Ransomware remains a major threat for organizations. Despite a lot of research done, existing solutions still have at least two shortcomings. (I) Slow detection time: by the time we realize that the system is under ransomware attack, almost all files have been encrypted. (II) Without a ransomwareaware backup scheme: Most existing systems, in particular those in SMEs (small and medium enterprises), do not have a proper backup system. Even they have it, either it is not a remote-site backup (i.e., files in the backup system may also be encrypted) or it is not designed for ransomware attacks. In this paper, …

"Design For Co-Design" In A Computer Science Curriculum Research-Practice Partnership, Victor R. Lee, Jody Clarke-Midura, Jessica F. Shumway, Mimi Recker

Publications

This paper reports on a study of the dynamics of a Research-Practice Partnership (RPP) oriented around design, specifically the co-design model. The RPP is focused on supporting elementary school computer science (CS) instruction by involving paraprofessional educators and teachers in curricular co-design. A problem of practice addressed is that few elementary educators have backgrounds in teaching CS and have limited available instructional time and budget for CS. The co-design strategy entailed highlighting CS concepts in the mathematics curriculum during classroom instruction and designing computer lab lessons that explored related ideas through programming. Analyses focused on tensions within RPP interaction dynamics …

Predictors Of Email Response: Determinants Of The Intention Of Not Following Security Recommendations, Miguel Angel Toro-Jarrin

Engineering Management & Systems Engineering Theses & Dissertations

Organizations and government leaders are concerned about cyber incidents. For some time, researchers have studied what motivates people to act in ways that put the confidentiality, integrity, and availability of information in organizations at risk. Still, several areas remained unexplored, including the role of employees’ evaluation of the organizational systems and the role of value orientation at work as precursors of secure and insecure actions in relation to information technologies (information security [IS] action). The objective of this research project was to examine how the evaluations of formal and informal security norms are associated with the intention to follow them …

Adaptive Risk Network Dependency Analysis Of Complex Hierarchical Systems, Katherine L. Smith

Computational Modeling & Simulation Engineering Theses & Dissertations

Recently the number, variety, and complexity of interconnected systems have been increasing while the resources available to increase resilience of those systems have been decreasing. Therefore, it has become increasingly important to quantify the effects of risks and the resulting disruptions over time as they ripple through networks of systems. This dissertation presents a novel modeling and simulation methodology which quantifies resilience, as impact on performance over time, and risk, as the impact of probabilistic disruptions. This work includes four major contributions over the state-of-the-art which are: (1) cyclic dependencies are captured by separation of performance variables into layers which …

Interpreting Trajectories From Multiple Views: A Hierarchical Self-Attention Network For Estimating The Time Of Arrival, Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical …

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning …

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning …

Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal …

Verifying Neural Networks Against Backdoor Attacks, Long Hong Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is …

Multimodal Private Signatures, Khoa Nguyen, Fuchun Guo, Willy Susilo, Guomin Yang

Research Collection School Of Computing and Information Systems

We introduce Multimodal Private Signature (MPS) - an anonymous signature system that offers a novel accountability feature: it allows a designated opening authority to learn some partial information op about the signer’s identity id, and nothing beyond. Such partial information can flexibly be defined as op = id (as in group signatures), or as op = 0 (like in ring signatures), or more generally, as op = Gj (id), where Gj (·) is a certain disclosing function. Importantly, the value of op is known in advance by the signer, and hence, the latter can decide whether she/he wants to disclose …

Self-Checking Deep Neural Networks For Anomalies And Adversaries In Deployment, Yan Xiao, Ivan Beschastnikh, Yun Lin, Rajdeep Singh Hundal, Xiaofei Xie, David S. Rosenblum, Jin Song Dong

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly unreliable, which raises significant concerns about their use in critical domains. In this work, we propose that runtime DNN mistakes can be quickly detected and properly dealt with in deployment, especially in settings like self-driving vehicles. Just as software engineering (SE) community has developed effective mechanisms and techniques to monitor and check programmed components, our previous work, SelfChecker, is designed to monitor and correct DNN predictions given unintended abnormal test data. SelfChecker triggers an alarm if the decisions given by the internal layer features of the model …

Andea: Anomaly And Novelty Detection, Explanation, And Accommodation, Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbin Cao, Thomas G. Dietterich

Research Collection School Of Computing and Information Systems

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, …

Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the …

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

Research Collection School Of Computing and Information Systems

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …

Simple And Optimal Stochastic Gradient Methods For Nonsmooth Nonconvex Optimization, Zhize Li, Jian Li

Research Collection School Of Computing and Information Systems

We propose and analyze several stochastic gradient algorithms for finding stationary points or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and online optimization problems. First, we propose a simple proximal stochastic gradient algorithm based on variance reduction called ProxSVRG+. We provide a clean and tight analysis of ProxSVRG+, which shows that it outperforms the deterministic proximal gradient descent (ProxGD) for a wide range of minibatch sizes, hence solves an open problem proposed in Reddi et al. (2016b). Also, ProxSVRG+ uses much less proximal oracle calls than ProxSVRG (Reddi et al., 2016b) and extends to the online setting by …

Sound And Complete Certificates For Quantitative Termination Analysis Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the quantitative problem of obtaining lower-bounds on the probability of termination of a given non-deterministic probabilistic program. Specifically, given a non-termination threshold p∈[0,1], we aim for certificates proving that the program terminates with probability at least 1−p. The basic idea of our approach is to find a terminating stochastic invariant, i.e. a subset SI of program states such that (i) the probability of the program ever leaving SI is no more than p, and (ii) almost-surely, the program either leaves SI or terminates.While stochastic invariants are already well-known, we provide the first proof that the idea above is …

Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention …

Systems And Methods For Contrastive Learning With Self-Labeling Refinement, Pan Zhou, Caiming Xiong, Steven Hoi

Research Collection School Of Computing and Information Systems

Embodiments described herein provide a contrastive learning mechanism with self - labeling refinement , which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning . Specifically , the contrastive learning framework includes a self - labeling refinery module to explicitly generate accurate labels , and a momentum mix - up module to increase similarity between a query and its positive , which in turn implicitly improves label accuracy.

Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

We study the maximum capture problem in facility location under random utility models, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured user demand is maximized, assuming that each customer chooses among all available facilities according to a random utility maximization model. We employ the generalized extreme value (GEV) family of discrete choice models and show that the objective function in this context is monotonic and submodular. This finding implies that a simple greedy heuristic can always guarantee a (1−1/e) approximation solution. We further develop a new algorithm combining a greedy heuristic, …

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …

Reachability And Turn Constraint Paths, Sabrina Wallace

UNLV Theses, Dissertations, Professional Papers, and Capstones

Problems dealing with the development of efficient algorithms for constructing collision-free paths have been explored extensively. We review existing algorithms for constructing collision-free paths under turn-angle constraints. We examine the problem of computing collision-free paths in the presence of polygonal obstacles. We present an algorithm for identifying the placement of a source vertex so that the maximum number of obstacle vertices can be reached via the shortest path tree under turn-angle requirements. We also present some experimental results on the construction of collision-free paths in the presence of polygonal obstacles.

Structure Aware Smart Encoding And Decoding Of Information In Dna, Shoshanna Llewellyn

Boise State University Theses and Dissertations

Our increasingly information driven world is growing the demand for new storage technologies. Current estimates place the total storage demands exceeding the supply of usable silicon by 2040 [1]. DNA is an attractive technology due to its incredible density, almost negligible energy requirements, and data retention measured in centuries [1]. DNA does, however, come with new challenges. It is an organic compound with complex internal interactions which complicate the design and synthesis of DNA sequences for the purpose of data storage. In this work we demonstrate a new encoding-decoding process that accounts for some of the challenges in encoding and …

The Ukicer 2022 Conference Poster: Techmate: A Best Practice Toolkit For Driving Sustainable Acceleration Towards Gender Equality In Technology Disciplines In Heis., Alina Berry

Conference papers

TechMate is a research project that is being developed to enhance gender balance in technology disciplines, in particular computing higher education in Ireland and beyond. Gender imbalance in computing education is a well-known issue: in Ireland, less than 15% of the student population in computer science, ICT and related disciplines are women. Despite a significant amount of research and practical work conducted in the recent decades, the problem still persists and this research initiative aims to improve the situation.

Among the main aims of this project, there is a development of a toolkit to drive sustainable acceleration towards gender equality …

Towards Making Transformer-Based Language Models Learn How Children Learn, Yousra Mahdy

Boise State University Theses and Dissertations

Transformer-based Language Models (LMs), learn contextual meanings for words using a huge amount of unlabeled text data. These models show outstanding performance on various Natural Language Processing (NLP) tasks. However, what the LMs learn is far from what the meaning is for humans, partly due to the fact that humans can differentiate between concrete and abstract words, but language models make no distinction. Concrete words are words that have a physical representation in the world such as “chair”, while abstract words are ideas such as “democracy”. The process of learning word meanings starts from early childhood when children acquire their …

Ontology-Based Formal Approach For Safety And Security Verification Of Industrial Control Systems, Ramesh Neupane

Boise State University Theses and Dissertations

Control logics, as part of the Industrial Control Systems (ICS), are used to control the physical processes of the critical infrastructures such as power plants, water, and gas distribution, etc. Most commonly, the Programmable Logic Controller (PLC) manages these processes through actuators based on information received from sensor readings. Any safety issues or cyberattacks on these systems may have catastrophic consequences on human lives and the environment. In an effort to improve the resilience and security of control logics, this thesis provides algorithms and tools to formally define the safety and security requirements w.r.t. the physical processes, and the industrial …