Physical Sciences and Mathematics Commons^™

Conversa: A Community Of Conversation, Leandro Silveira, Willian Antunes De Sousa, Richard Welbert Silva Biagi, Patricia Correia, Lucas Freire

ICT

In the past decade, Dublin has seen increasing development in many sectors, remarkably in the Education industry. The country has attracted many English learning seekers, students that come to Dublin to live, learn the language, and the local culture. However, exciting this adventure might seem, foreigners may still struggle to overcome a set of difficulties when learning another language in a different environment.

For this reason, this project will try to develop an app that can help students meet new people with the primary objective to learn and practice English with people nearby. Users will be able to find people …

Local Dimensions And Quantization Dimensions In Dynamical Systems, Mrinal Kanti Roychowdhury, Bilel Selmi

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Let μ be a Borel probability measure generated by a hyperbolic recurrent iterated function system defined on a nonempty compact subset of Rk. We study the Hausdorff and the packing dimensions, and the quantization dimensions of μ with respect to the geometric mean error. The results establish the connections with various dimensions of the measure μ and generalize many known results about local dimensions and quantization dimensions of measures.

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn

Research Collection School Of Computing and Information Systems

Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …

Non-Conventional Vehicles As A Way Towards Carbon Neutrality In Iceland, Julia Sokolowska

Independent Study Project (ISP) Collection

Paris Agreement’s chief objective is to protect the Earth and its inhabitants from a point of no return, when the effects of climate change will be so intense that they will shift the equilibrium of ecosystems. The distinctiveness of this international environmental treaty is that it does not impose climate change mitigation measures, but rather allows nation states to create their own set of measures, the NDCs, to reach the global warming of ‘well below 2oC’ by the end of the century. Thus, Iceland has submitted its own NDC, the Climate Action Plan 2018-2030, which has an ambitious goal of …

Math 310: Applied Regression Analysis, Yu Wang

Open Educational Resources

Introduce the different linear statistical models and develop critical thinking for statistical modeling in scientific and policy contexts; Apply statistical computer software tools to develop useful data analysis skills based on the use of linear regression models. Topics to be covered: simple linear regression, multiple regression, nonlinear regression and logistic regression models; Random and mixed effects models; The application of statistical software tools.

Energy Spectrum Of Linear Internal Wave Field In The Vicinity Of Continental Slope, Ranis N. Ibragimov, Austin Biondi, Nathan Arndt, Maria Castillo, Guang Lin, Vesselin Vatchev, Sheng Zhang

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

The purpose of the research was to investigate two-dimensional modeling of efficiency of mixing, resulting from the reflection of a linear internal wave field (IWF) off a continental slope. Efficiency of deep ocean mixing was associated with the energy balance of the radiating IWF into an interior of the ocean in the vicinity of a sloping bottom topography. Since waves are generated not only at the fundamental frequency but also at all of its harmonics ωn = nω less than buoyancy frequency N and greater than Coriolis frequency f, our analysis includes, in general, an infinite number of …

Gravitational-Wave Constraints On The Equatorial Ellipticity Of Millisecond Pulsars, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, Teviet Creighton, Mario C. Diaz, S. Mukherjee, V. Quetschke, Malik Rakhmanov, K. E. Ramirez, W. H. Wang

Physics and Astronomy Faculty Publications and Presentations

We present a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437−4715, PSR J0711−6830, and PSR J0737−3039A) and two young pulsars: the Crab pulsar (J0534+2200) and the Vela pulsar (J0835−4510). We use data from the third observing run of Advanced LIGO and Virgo combined with data from their first and second observing runs. For the first time we are able to match (for PSR J0437−4715) or surpass (for PSR J0711−6830) the indirect limits on gravitational-wave emission from recycled pulsars inferred from their observed spin-downs, and constrain their equatorial ellipticities to be less than 10−8 …

Interests And Priorities In Sockeye Salmon Management: How Are Policies Enacted And Interpreted On Three Alaskan Rivers?, Jake P. Palazzi

University Honors Theses

The large export abundance of Alaskan salmon is well documented, and many studies have been performed to assess the economic and environmental viability of the industry and its management. Less research has been done to characterize how state intentions regarding fisheries allocation are conceived of by management or perceived by vulnerable groups in the user pool. This study seeks to qualitatively characterize the disconnect between state and Native Alaskan perceptions of management effectiveness, public interest, and Native Alaskan involvement using interviews. Results showed that Native Alaskan and state manager respondents had very different perceptions of management effectiveness and equity. When …

Energy Demand And Economic Growth: Public Opinion And Mutual Exclusivity, Nicholas L. Silvis

Student Publications

The world is currently undergoing an energy transition from primarily fossil fuels to cleaner energy. The developing world is becoming more advanced, spawning relentless economic growth and an increase in energy consumption. Energy demand and economic growth are inextricably linked which poses a paradoxical question about future economic growth during a period of energy transition. Unfortunately, the transition requires large upfront costs with no guaranteed net benefit. A multitude of studies depict the impact of education, party identification, and age on how individuals perceive alternative energy. This study shows that views on governmental spending and party membership have a paradoxical …

Lenskit For Python: Next-Generation Software For Recommender Systems Experiments, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible experiments that make use of the large and growing PyData and Scientific Python ecosystem, including scikit-learn, and TensorFlow. To …

Evaluating Stochastic Rankings With Expected Exposure, Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

Computer Science Faculty Publications and Presentations

We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item should receive more or less expected exposure than any other item of the same relevance grade. We argue that this principle is desirable for many retrieval objectives and scenarios, including topical diversity and fair ranking. Leveraging user models from existing retrieval metrics, we propose a general evaluation methodology based on expected exposure and draw connections to related …

Assessing The Impact Of A New Inlet Created By 2012 Hurricane Sandy On The Intensity Of Algae Blooms In Bellport Bay Ny, Ryan A. Wagner

Student Publications

Harmful algae blooms (HABs) are a growing ecosystem health issue in environments worldwide, driven by excess nitrogen runoff (Eutrophication) alongside high summer temperatures. HABs strip oxygen from the environment and create toxic environments that impact other primary producers, fish, birds, mammals, reptiles, and any other organisms that enter an affected body of water. The purpose of this study is to analyze the impact of a new inlet, created by Hurricane Sandy in Long Island’s Bellport Bay, on the concentration of algae blooms during peak blooming periods (Jul-Aug) to inform ecosystem-based management. Google Earth Engine Code Editor and 2008-2017 Landsat 5-8 …

Towards Locality-Aware Meta-Learning Of Tail Node Embeddings On Networks, Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embedding. In this paper, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides …

Attribute-Based Fine-Grained Access Control For Outscored Private Set Intersection Computation, Mohammad Ali, Mohajeri Javad, Mohammad-Reza Sadeghi, Ximeng Liu

Research Collection School Of Computing and Information Systems

Private set intersection (PSI) is a fundamental cryptographic protocol which has a wide range of applications. It enables two clients to compute the intersection of their private datasets without revealing non-matching elements. The advent of cloud computing drives the ambition to reduce computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, some cryptographic methods should be applied to maintain the confidentiality of datasets. But, in doing so, data owners may be excluded from access control on their outsourced datasets. Therefore, to control access rights and to interact with authorized users, they have to …

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …

Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He

Research Collection School Of Computing and Information Systems

Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real-world photographs like an artist. To this end, we propose a two-stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high-quality smoothing results in a label-free manner. Together with two other semantic-aware losses, the abstraction stage imposes different forms of regularization for creating cartoon-like flattened images. …

Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

We focus on the problem of simulating the haptic infrastructure of a virtual environment (i.e. walls, doors). Our approach relies on multiple ZoomWalls---autonomous robotic encounter-type haptic wall-shaped props---that coordinate to provide haptic feedback for room-scale virtual reality. Based on a user's movement through the physical space, ZoomWall props are coordinated through a predict-and-dispatch architecture to provide just-in-time haptic feedback for objects the user is about to touch. To refine our system, we conducted simulation studies of different prediction algorithms, which helped us to refine our algorithmic approach to realize the physical ZoomWall prototype. Finally, we evaluated our system through a …

Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan

Research Collection School Of Computing and Information Systems

This Innovative Practice Full Paper discusses our course design on software architecture to meet the learning expectations of two groups of software engineers. Software engineers with working experiences frequently find themselves the need to upskill in their lifelong learning journey. Their learning expectations are shaped not just by their need to know but also other learning characteristics such as their working experiences. In many cases, we design courses based on the required learning outcomes and assessment criteria. In this paper, we wish to find out whether our course design on software architecture has met the learning expectations of our students …

Using Student Perceptions To Design Smart Class Participation Tools: A Technology Framework, Swapna Gottipati, Shankararaman, Venky, Mark Wei Jie Ng

Research Collection School Of Computing and Information Systems

Our research full paper studies the perceptions of students and proposes technology design framework for smart class participation tools. Participation in classroom discussions has been observed to improve student comprehension and performance. In our contemporary tertiary educational context, student participation is characterised in mainly two forms; inclass discussions and online forums. Both forms of participation generate voluminous amounts of knowledge. However, the present difficulty in capturing and analysing these forms of participation leads to loss of knowledge and insights, which otherwise could be very useful. This study aims to analyse ways to improve data capture as well as data analysis …

Viscene: A Collaborative Authoring Tool For Scene Descriptions In Videos, Rosiana Natalie, Ebrima Jarjue, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions can make the visual content in videos accessible to people with visual impairments. However, the majority of the online videos lack audio descriptions due in part to the shortage of experts who can create high-quality descriptions. We present ViScene, a web-based authoring tool that taps into the larger pool of sighted non-experts to help them generate high-quality descriptions via two feedback mechanisms - succinct visualizations and comments from an expert. Through a mixed-design study with N = 6 participants, we explore the usability of ViScene and the quality of the descriptions created by sighted non-experts with and without …

Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across …

Regression Testing Of Massively Multiplayer Online Role-Playing Games, Yuechen Wu, Yingfeng Chen, Xiaofei Xie, Bing Yu, Changjie Fan, Lei Ma

Research Collection School Of Computing and Information Systems

Regression testing aims to check the functionality consistency during software evolution. Although general regression testing has been extensively studied, regression testing in the context of video games, especially Massively Multiplayer Online Role-Playing Games (MMORPGs), is largely untouched so far. One big challenge is that game testing requires a certain level of intelligence in generating suitable action sequences among the huge search space, to accomplish complex tasks in the MMORPG. Existing game testing mainly relies on either the manual playing or manual scripting, which are labor-intensive and time-consuming. Even worse, it is often unable to satisfy the frequent industrial game evolution. …

Interpretable Embedding For Ad-Hoc Video Search, Jiaxin Wu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Answering query with semantic concepts has long been the mainstream approach for video search. Until recently, its performance is surpassed by concept-free approach, which embeds queries in a joint space as videos. Nevertheless, the embedded features as well as search results are not interpretable, hindering subsequent steps in video browsing and query reformulation. This paper integrates feature embedding and concept interpretation into a neural network for unified dual-task learning. In this way, an embedding is associated with a list of semantic concepts as an interpretation of video content. This paper empirically demonstrates that, by using either the embedding features or …

Impacts Of Forest Management And Timber Harvest Practices On Karst Critical Zone Processes In Tongass National Forest, Alaska, Anna Gwendolyn Harris

Masters Theses & Specialist Projects

This study characterizes the throughfall, hydrogeochemistry, dissolution rates, and carbon sources of two proximate temperate rainforest cave systems within the Tongass National Forest in Southeast Alaska (Tongass). Study sites include: an old-growth forest, characterized by having never been logged (containing Walkabout Cave system); and a previously logged – within thirty years, second-growth forest (containing Zina Cave system). Precipitation data were recorded over a five-month period at 10-minute intervals, to understand the effects of throughfall between the altering old and second-growth canopies. At each major spring for the two cave systems, high-resolution data were collected from June 29 through November 21, …

Developing Novel Tale-Based Rapid Detection Of Pathogenic Dna Utilizing 2d Graphene Oxide Nanosheets And Quantum Dots, Narayan Neupane

Masters Theses & Specialist Projects

A faster method of quantitative detection of specific dsDNA of pathogenic bacteria such as the Shiga toxin 2 gene (stx2) present in E. coli O157:H7 is of great importance. There is a need for a simple and facile method for sensitive detection of pathogenic gene which is crucial for the prevention and earlier treatment of any infectious diseases. A Transcriptional Activator-Like Effector (TALE) is a novel class of DNA-binding proteins with the unique modularity, flexibility and easy programmability compared to previously discovered DNA- binding proteins. TALEs can bind to any DNA sequences through its unique variable di- residues …

Some Generalizations Of Classical Integer Sequences Arising In Combinatorial Representation Theory, Sasha Verona Malone

Masters Theses & Specialist Projects

There exists a natural correspondence between the bases for a given finite-dimensional representation of a complex semisimple Lie algebra and a certain collection of finite edge-colored ranked posets, laid out by Donnelly, et al. in, for instance, [Don03]. In this correspondence, the Serre relations on the Chevalley generators of the given Lie algebra are realized as conditions on coefficients assigned to poset edges. These conditions are the so-called diamond, crossing, and structure relations (hereinafter DCS relations.) New representation constructions of Lie algebras may thus be obtained by utilizing edge-colored ranked posets. Of particular combinatorial interest are those representations whose corresponding …

Lis: Lightweight Signature Schemes For Continuous Message Authentication In Cyber-Physical Systems, Zheng Yang, Chenglu Jin, Yangguang Tian, Junyu Lai, Jianying Zhou

Research Collection School Of Computing and Information Systems

Cyber-Physical Systems (CPS) provide the foundation of our critical infrastructures, which form the basis of emerging and future smart services and improve our quality of life in many areas. In such CPS, sensor data is transmitted over the network to the controller, which will make real-time control decisions according to the received sensor data. Due to the existence of spoofing attacks (more specifically to CPS, false data injection attacks), one has to protect the authenticity and integrity of the transmitted data. For example, a digital signature can be used to solve this issue. However, the resource-constrained field devices like sensors …

White-Box Fairness Testing Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, Dai Ting

Research Collection School Of Computing and Information Systems

Although deep neural networks (DNNs) have demonstrated astonishing performance in many applications, there are still concerns on their dependability. One desirable property of DNN for applications with societal impact is fairness (i.e., non-discrimination). In this work, we propose a scalable approach for searching individual discriminatory instances of DNN. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable than existing methods. Experimental results show that our approach explores the search space more effectively (9 times) and generates much more individual discriminatory instances (25 times) using much less time (half …

Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler

Engineering Technology Faculty Publications

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using …

The Utility Of Multiple Structure Torsion Angle Alignment In Protein Active Site Description (Asd), Devaun L. Mcfarland

Theses and Dissertations

Proteins are responsible for various functions throughout organisms, or within the systems, they operate. Active-sites or functional/ binding sites are regions responsible for activity in a protein; they serve as a catalyst for reactions, attach or bind to other molecules (ligands), and maintain function. With the profusion of protein sequence and structure data, it's increasingly relevant to develop automated methods of identifying and investigating active-sites for proteins. Active-sites identification will have a direct impact: in better understanding molecular basis for diseases, assisting in drug design, the study of targeting mutants, and for functional annotation of unknown proteins. The proper knowledge …