Physical Sciences and Mathematics Commons^™

Engage!: Co-Designing Search Engine Result Pages To Foster Interactions, Garrett Allen, Ben Peterson, Dhanush Kumar Ratakonda, Mostofa Najmus Sakib, Jerry Alan Fails, Casey Kennington, Katherine Landau Wright, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children’s engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children’s preferences and determine what children (ages 6–12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP …

5Th Kidrec Workshop: Search And Recommendation Technology Through The Lens Of A Teacher, Monica Landoni, Theo Huibers, Maria Soledad Pera, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

In this past year, the role of technology to support education has been more prominent than ever. This has prompted us to focus the 5^th Edition of the International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) around a major stakeholder when it comes to technology adoption for the classroom: the teacher. Much like in the previous editions of the workshop, our priority remains understanding what is good when it comes to information retrieval systems for children, this time from the perspectives of teachers. In order to control scope of our discussion and …

Using Service-Learning In Graduate Curriculum To Address Teenagers' Vulnerability To Web Misinformation, Francesca Spezzano

Computer Science Faculty Publications and Presentations

We report on how we implemented service-learning (S-L) in a CS graduate class to improve student understanding of the class materials and provide a service to the community, i.e., addressing teenagers’ vulnerability to Web misinformation. We show how S-L benefits CS students in their course theory understanding and personal skills development, while teenagers’ news media literacy and misinformation detection accuracy were positively impacted.

Distributing Participation In Design: Addressing Challenges Of A Global Pandemic, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

Participatory Design (PD) – whose inclusive benefits are broadly recognised in design – can be very challenging, especially when involving children. The recent COVID-19 pandemic has given rise to further barriers to PD with such groups. One key barrier is the advent of social distancing and government-imposed social restrictions due to the additional risks posed for e.g. children and families vulnerable to COVID-19. This disrupts traditional in-person PD (which involves close socio-emotional and often physical collaboration between participants and researchers). However, alongside such barriers, we have identified opportunities for new and augmented approaches to PD across distributed geographies, backgrounds, ages …

Energy On Spheres And Discreteness Of Minimizing Measures, Dmitriy Bilyk, Alexey Glazyrin, Ryan Matzke, Josiah Park, Oleksandr Vlasiuk

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In the present paper we study the minimization of energy integrals on the sphere with a focus on an interesting clustering phenomenon: for certain types of potentials, optimal measures are discrete or are supported on small sets. In particular, we prove that the support of any minimizer of the p-frame energy has empty interior whenever p is not an even integer. A similar effect is also demonstrated for energies with analytic potentials which are not positive definite. In addition, we establish the existence of discrete minimizers for a large class of energies, which includes energies with polynomial potentials.

First Train Timetabling And Bus Service Bridging In Intermodal Bus-And-Train Transit Networks, Liujiang Kang, Hao Li, Huijun Sun, Jianjun Wu, Zhiguang Cao, Nsabimana Buhigiro

Research Collection School Of Computing and Information Systems

Subway system is the main mode of transportation for city dwellers and is a quite signif-icant backbone to a city's operations. One of the challenges of subway network operation is the scheduling of the first trains each morning and its impact on transfers. To deal with this challenge, some cities (e.g. Beijing) use bus 'bridging' services, temporarily substitut -ing segments of the subway network. The present paper optimally identifies when to start each train and bus bridging service in an intermodal transit network. Starting from a mixed integer nonlinear programming model for the first train timetabling problem, we linearize and …

Learning From The Master: Distilling Cross-Modal Advanced Knowledge For Lip Reading, Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal data could lead to a learning ambiguity and thus limits the performance of lip reading. In this paper, we propose a novel collaborative framework for lip reading, and two aspects of issues are considered: 1) the teacher should understand bi-modal knowledge to possibly bridge the inherent cross-modal gap; 2) …

A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method For Efficient Optimization And Generalization, Pan Zhou, Xiao-Tong Yuan, Lin Zhouchen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g. least squares and logistic/softmax regression. HSDMPG enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of n components, HSDMPG attains an -optimization-error within O κ …

Proving Non-Termination By Program Reversal, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We present a new approach to proving non-termination of non-deterministic integer programs. Our technique is rather simple but efficient. It relies on a purely syntactic reversal of the program's transition system followed by a constraint-based invariant synthesis with constraints coming from both the original and the reversed transition system. The latter task is performed by a simple call to an off-the-shelf SMT-solver, which allows us to leverage the latest advances in SMT-solving. Moreover, our method offers a combination of features not present (as a whole) in previous approaches: it handles programs with non-determinism, provides relative completeness guarantees and supports programs …

Adaptive Aggregation Networks For Class-Incremental Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets) in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two …

How-To Present News On Social Media: A Causal Analysis Of Editing News Headlines For Boosting User Engagement, Kunwoo Park, Haewoon Kwak, Jisun An, Sanjay Chawla

Research Collection School Of Computing and Information Systems

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those …

Minimum Coresets For Maxima Representation Of Multidimensional Data, Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Coresets are succinct summaries of large datasets such that, for a given problem, the solution obtained from a coreset is provably competitive with the solution obtained from the full dataset. As such, coreset-based data summarization techniques have been successfully applied to various problems, e.g., geometric optimization, clustering, and approximate query processing, for scaling them up to massive data. In this paper, we study coresets for the maxima representation of multidimensional data: Given a set �� of points in R �� , where �� is a small constant, and an error parameter �� ∈ (0, 1), a subset �� ⊆ �� …

On M-Impact Regions And Standing Top-K Influence Problems, Bo Tang, Kyriakos Mouratidis, Mingji Han

Research Collection School Of Computing and Information Systems

In this paper, we study the ��-impact region problem (mIR). In a context where users look for available products with top-�� queries, mIR identifies the part of the product space that attracts the most user attention. Specifically, mIR determines the kind of attribute values that lead a (new or existing) product to the top-�� result for at least a fraction of the user population. mIR has several applications, ranging from effective marketing to product improvement. Importantly, it also leads to (exact and efficient) solutions for standing top-�� impact problems, which were previously solved heuristically only, or whose current solutions face …

Ganmut: Learning Interpretable Conditional Space For A Gamut Of Emotions, S. D'Apolito, D.P. Paundel, Zhiwu Huang, A.R. Vergara, Gool L. Van

Research Collection School Of Computing and Information Systems

Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labeling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability. In this paper, we propose a novel GAN-based framework which learns an expressive and interpretable conditional space (usable as a label space) of emotions, instead of conditioning on …

Ultrapin: Inferring Pin Entries Via Ultrasound, Liu, Ximing, Robert H. Deng, Robert H. Deng

Research Collection School Of Computing and Information Systems

While PIN-based user authentication systems such as ATM have long been considered to be secure enough, they are facing new attacks, named UltraPIN, which can be launched from commodity smartphones. As a target user enters a PIN on a PIN-based user authentication system, an attacker may use UltraPIN to infer the PIN from a short distance (50 cm to 100 cm). In this process, UltraPIN leverages smartphone speakers to issue human-inaudible ultrasound signals and uses smartphone microphones to keep recording acoustic signals. It applies a series of signal processing techniques to extract high-quality feature vectors from low-energy and high-noise signals …

Secure Repackage-Proofing Framework For Android Apps Using Collatz Conjecture, Haoyu Ma, Shijia Li, Debin Gao, Chunfu Jia

Research Collection School Of Computing and Information Systems

App repackaging has been raising serious concerns about the health of the Android ecosystem, and repackage-proofing is an important mitigation against threat of such attacks. However, existing app repackage-proofing schemes were only evaluated against trivial adversaries simulated using analyzers for other purposes (e.g., disclosing privacy leakage vulnerabilities), hence were shown “effective” mainly because their key programming features were not even supported by those toolkits. Furthermore, existing works have also neglected dynamic adversaries capable of manipulating victim apps at runtime, making them vulnerable against such stronger opponents. In this paper, we propose a novel repackage-proofing framework, which deploys distributed detection and …

A Large Scale Study Of Long-Time Contributor Prediction For Github Projects, Lingfeng Bao, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

The continuous contributions made by long time contributors (LTCs) are a key factor enabling open source software (OSS) projects to be successful and survival. We study Github as it has a large number of OSS projects and millions of contributors, which enables the study of the transition from newcomers to LTCs. In this paper, we investigate whether we can effectively predict newcomers in OSS projects to be LTCs based on their activity data that is collected from Github. We collect Github data from GHTorrent, a mirror of Github data. We select the most popular 917 projects, which contain 75,046 contributors. …

Spatially-Invariant Style-Codes Controlled Makeup Transfer, Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Transferring makeup from the misaligned reference image is challenging. Previous methods overcome this barrier by computing pixel-wise correspondences between two images, which is inaccurate and computational-expensive. In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process. To this end, we propose a Style-based Controllable GAN model that consists of three components, each of which corresponds to target style-code encoding, face identity features extraction, and makeup fusion, respectively. In particular, a Part-specific Style Encoder encodes the component-wise makeup style of the reference image into a style-code in an intermediate latent space …

Grand-Vision: An Intelligent System For Optimized Deployment Scheduling Of Law Enforcement Agents, Jonathan Chase, Tran Phong, Kang Long, Tony Le, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through …

Lattice-Based Remote User Authentication From Reusable Fuzzy Signature, Yangguang Tian, Yingjiu Li, Robert H. Deng, Binanda Sengupta, Guomin Yang

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new construction of reusable fuzzy signature based remote user authentication that is secure against quantum computers. We investigate the reusability of fuzzy signature, and we prove that the fuzzy signature schemes provide biometrics reusability (aka. reusable fuzzy signature). We define formal security models for the proposed construction, and we prove that it achieves user authenticity and user privacy. The proposed construction ensures: 1) a user’s biometrics can be securely reused in remote user authentication; 2) a third party having access to the communication channel between a user and the authentication server cannot identify the …

Boosting Video Representation Learning With Multi-Faceted Integration, Zhaofan Qiu, Yao Ting, Chong-Wah Ngo, Xiao-Ping Zhang, Dong Wu, Tao Mei

Research Collection School Of Computing and Information Systems

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the …

An Economic Analysis Of Rebates Conditional On Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang

Research Collection School Of Computing and Information Systems

Strategic sellers on some online selling platforms have recently been using a conditional-rebate strategy to manipulate product reviews under which only purchasing consumers who post positive reviews online are eligible to redeem the rebate. A key concern for the conditional rebate is that it can easily induce fake reviews, which might be harmful to consumers and society. We develop a microbehavioral model capturing consumers’ review-sharing benefit, review-posting cost, and moral cost of lying to examine the seller’s optimal pricing and rebate decisions. We derive three equilibria: the no-rebate, organic-review equilibrium; the low-rebate, boosted-authentic-review equilibrium; and the high-rebate, partially-fake-review equilibrium. We …

An Analysis Of The Impacts Of Climate Change On Food Security In The Albertine Rift Of East Africa, Malcolm Jacob

Sustainability and Social Justice

As one of the most densely populated regions on the continent of Africa, the Albertine Rift (consisting of parts of Rwanda, Uganda, and the eastern DRC) faces ongoing problems providing enough food for its people through crop production, livestock husbandry, and other forms of food production. Even more troubling for the future is that anthropogenic climate change is expected to significantly exacerbate food insecurity. This paper addresses one central question: how will climate change impact food security in the Albertine Rift? Based on an analysis of available data, this paper finds that policymakers should listen closely to local farmers and …

Backarc Lithospheric Thickness And Serpentine Stability Control Slab-Mantle Coupling Depths In Subduction Zones, Buchanan C. Kerswell, Matthew J. Kohn, Taras V. Gerya

Geosciences Faculty Publications and Presentations

A key feature of subduction zone geodynamics and thermal structure is the point at which the slab and mantle mechanically couple. This point defines the depth at which traction between slab and mantle begins to drive mantle wedge circulation and also corresponds with a rapid increase in temperature along the slab-mantle interface. Here, we consider the effects of the backarc thermal structure and slab thermal parameter on coupling depth using two-dimensional thermomechanical models of oceanic-continental convergent margins. Coupling depth is strongly correlated with backarc lithospheric thickness, and weakly correlated with slab thermal parameter. Slab-mantle coupling becomes significant where weak, hydrous …

The Effects Of Advanced Analytics And Machine Learning On The Transportation Of Natural Gas, Bj Stigall

Doctoral Dissertations and Projects

This qualitative single case study describes the effects of an advanced analytic and machine learning system (AAML) has on the transportation of natural gas pipelines and the causes for failure to fully utilize the advanced analytic and machine learning system. This study's guiding theory was the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Transformation Leadership. The factors for failure to fully utilize AAML systems were studied, and the factors that made the AAML system successful were also examined. Data were collected through participant interviews. This study indicates that the primary factors for failure to fully utilize …

Decreasing The Miss Rate And Eliminating The Performance Penalty Of A Data Filter Cache, Michael Stokes, David Whalley, Soner Onder

Michigan Tech Publications

While data filter caches (DFCs) have been shown to be effective at reducing data access energy, they have not been adopted in processors due to the associated performance penalty caused by high DFC miss rates. In this article, we present a design that both decreases the DFC miss rate and completely eliminates the DFC performance penalty even for a level-one data cache (L1 DC) with a single cycle access time. First, we show that a DFC that lazily fills each word in a DFC line from an L1 DC only when the word is referenced is more energy-efficient than eagerly …

Design, Synthesis And Evaluation Of Molecules With Selective And Poly-Pharmacological Actions At D1r, D3r And Sigma Receptors, Pierpaolo Cordone

Dissertations, Theses, and Capstone Projects

The dopamine D3 receptor (D3R) is one of the most studied receptors involved in drug addiction. One of the most common strategies to treat substance use disorders is via D3R antagonism. The majority of the D3R antagonists synthesized so far have poor pharmacokinetic properties and/or lack selectivity toward D3R. In this thesis, the design, synthesis and biological evaluation of novel molecules that target the dopamine D1 receptor (D1R), D3R and the serendipitous discovery of molecules that target s receptors will be described.

Chapter 1 presents a survey of the fundamental pharmacology of D1R, D3R and s receptors and the therapeutic …

The Observable Supernova Rate In Galaxy–Galaxy Lensing Systems With The Tess Satellite, Benne Holwerda, S Knabel, R C. Steele, L Strolger, J Kielkopf, A Jacques, W Roemer

Faculty and Staff Scholarship

The Transiting Exoplanet Survey Satellite (TESS) is the latest observational effort to find exoplanets and map bright transient optical phenomena. Supernovae (SNe) are particularly interesting as cosmological standard candles for cosmological distance measures. The limiting magnitude of TESS strongly constrains SN detection to the very nearby Universe (m ∼ 19, z < 0.05). We explore the possibility that more distant SNe that are gravitationally lensed and magnified by a foreground galaxy can be detected by TESS, an opportunity to measure the time delay between light paths and constrain the Hubble constant independently. We estimate the rate of occurrence of such systems, assuming reasonable distributions of magnification, host dust attenuation, and redshift. There are approximately 16 Type Ia SNe (SNIa) and …

Predicting The Spectrum Of Ugc 2885, Rubin’S Galaxy With Machine Learning, Benne Holwerda, John F. Wu, William C. Keel, Jason Young, Ren Mullins, Joannah Hinz, K. E. Saavik Ford, Pauline Barmby, Rupali Chandar, Jeremy Bailin, Josh Peek, Tim Pickering, Torsten Böker

Faculty and Staff Scholarship

Wu & Peek predict SDSS-quality spectra based on Pan-STARRS broadband grizy images using machine learning (ML). In this article, we test their prediction for a unique object, UGC 2885 ("Rubin's galaxy"), the largest and most massive, isolated disk galaxy in the local universe (D < 100 Mpc). After obtaining the ML predicted spectrum, we compare it to all existing spectroscopic information that is comparable to an SDSS spectrum of the central region: two archival spectra, one extracted from the VIRUS-P observations of this galaxy, and a new, targeted MMT/Binospec observation. Agreement is qualitatively good, though the ML prediction prefers line ratios slightly more toward those of an active galactic nucleus (AGN), compared to archival and VIRUS-P observed values. The MMT/Binospec nuclear spectrum unequivocally shows strong emission lines except Hβ, the ratios of which are consistent with AGN activity. The ML approach to galaxy spectra may be a viable way to identify AGN supplementing NIR colors. How such a massive disk galaxy (M* = 1011 M⊙), which uncharacteristically shows no sign of interaction or mergers, manages to fuel its central …

Learn Biologically Meaningful Representation With Transfer Learning, Di He

Dissertations, Theses, and Capstone Projects

Machine learning has made significant contributions to bioinformatics and computational biol­ogy. In particular, supervised learning approaches have been widely used in solving problems such as bio­marker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super­ vised settings is not always desirable or possible, especially when using data­hunger, red­hot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap­ plications in this area.

In my dissertation, …