Physical Sciences and Mathematics Commons^™

Promoting Diversity In Academic Research Communities Through Multivariate Expert Recommendation, Omar Salman

Graduate Theses and Dissertations

Expert recommendation is the process of identifying individuals who have the appropriate knowledge and skills to achieve a specific task. It has been widely used in the educational environment mainly in the hiring process, paper-reviewer assignment, and assembling conference program committees. In this research, we highlight the problem of diversity and fair representation of underrepresented groups in expertise recommendation, factors that current expertise recommendation systems rarely consider. We introduce a novel way to model experts in academia by considering demographic attributes in addition to skills. We use the h-index score to quantify skills for a researcher and we identify five …

Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao

Graduate Theses and Dissertations

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected.

Cloud computing has become more and more popular in …

Paying Attention To Video Object Pattern Understanding, Wenguan Wang, Jianbing Shen, Xiankai Lu, Steven C. H. Hoi, Haibin Ling

Research Collection School Of Computing and Information Systems

This paper conducts a systematic study on the role of visual attention in video object pattern understanding. By elaborately annotating three popular video segmentation datasets (DAVIS) with dynamic eye-tracking data in the unsupervised video object segmentation (UVOS) setting. For the first time, we quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgments during dynamic, task-driven viewing. Such novel observations provide an in-depth insight of the underlying rationale behind video object pattens. Inspired by these findings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention …

Oesense: Employing Occlusion Effect For In-Ear Human Sensing, Dong Ma, Andrea Ferlini, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an …

Page: A Simple And Optimal Probabilistic Gradient Estimator For Nonconvex Optimization, Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtarik

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel stochastic gradient estimator---ProbAbilistic Gradient Estimator (PAGE)---for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p_t$ or reuses the previous gradient with a small adjustment, at a much lower computational cost, with probability $1-p_t$. We give a simple formula for the optimal choice of $p_t$. Moreover, we prove the first tight lower bound $\Omega(n+\frac{\sqrt{n}}{\epsilon^2})$ for nonconvex finite-sum problems, which also leads to a tight lower bound $\Omega(b+\frac{\sqrt{b}}{\epsilon^2})$ for nonconvex online problems, where …

Exploring Cross-Modality Utilization In Recommender Systems, Quoc Tuan Truong, Aghiles Salah, Thanh-Binh Tran, Jingyao Guo, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Multimodal recommender systems alleviate the sparsity of historical user-item interactions. They are commonly catalogued based on the type of auxiliary data (modality) they leverage, such as preference data plus user-network (social), user/item texts (textual), or item images (visual) respectively. One consequence of this categorization is the tendency for virtual walls to arise between modalities. For instance, a study involving images would compare to only baselines ostensibly designed for images. However, a closer look at existing models' statistical assumptions about any one modality would reveal that many could work just as well with other modalities. Therefore, we pursue a systematic investigation …

A Coprocessor-Based Introspection Framework Via Intel Management Engine, Lei Zhou, Fengwei Zhang, Jidong Xiao, Kevin Leach, Westley Weimer, Xuhua Ding, Guojun Wang

Research Collection School Of Computing and Information Systems

During the past decade, virtualization-based (e.g., virtual machine introspection) and hardware-assisted approaches (e.g., x86 SMM and ARM TrustZone) have been used to defend against low-level malware such as rootkits. However, these approaches either require a large Trusted Computing Base (TCB) or they must share CPU time with the operating system, disrupting normal execution. In this article, we propose an introspection framework called NIGHTHAWK that transparently checks system integrity and monitor the runtime state of target system. NIGHTHAWK leverages the Intel Management Engine (IME), a co-processor that runs in isolation from the main CPU. By using the IME, our approach has …

A Mean-Field Markov Decision Process Model For Spatial-Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang

Research Collection School Of Computing and Information Systems

Ride-sourcing services are increasingly popular because of their ability to accommodate on-demand travel needs. A critical issue faced by ride-sourcing platforms is the supply-demand imbalance, as a result of which drivers may spend substantial time on idle cruising and picking up remote passengers. Some platforms attempt to mitigate the imbalance by providing relocation guidance for idle drivers who may have their own self-relocation strategies and decline to follow the suggestions. Platforms then seek to induce drivers to system-desirable locations by offering them subsidies. This paper proposes a mean-field Markov decision process (MF-MDP) model to depict the dynamics in ride-sourcing markets …

Frameaxis: Characterizing Microframe Bias And Intensity With Word Embedding, Haewoon Kwak, Jisun An, Elise Jing Jing, Yong-Yeol Ahn

Research Collection School Of Computing and Information Systems

Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes (“microframes”) that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how …

Unified Conversational Recommendation Policy Learning Via Graph-Based Reinforcement Learning, Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, Wai Lam

Research Collection School Of Computing and Information Systems

Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversations. Reinforcement learning (RL) is widely adopted to learn conversational recommendation policies to decide what attributes to ask, which items to recommend, and when to ask or recommend, at each conversation turn. However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure. In the light of these challenges, we propose …

Designing A Health Coach-Augmented Mhealth System For The Secondary Prevention Of Coronary Heart Disease, Avijit Sengupta

USF Tampa Graduate Theses and Dissertations

This dissertation presents research that employs design science research (DSR) methodology to develop and evaluate a high-fidelity prototype of a home-based cardiac rehabilitation (HBCR) system to support self-management of chronic cardiovascular diseases like coronary heart disease (CHD) and to offer secondary prevention against other chronic diseases with similar risk factors. While the population of coronary heart disease (CHD) patients requiring cardiac rehabilitation (CR) continues to expand, lack of access and other barriers to center based cardiac rehabilitation (CBCR) presents a huge challenge. A mobile phone and wearable device based technological system can offer an HBCR program for CHD. By following …

Designing Targeted Mobile Advertising Campaigns, Kimia Keshanian

USF Tampa Graduate Theses and Dissertations

With the proliferation of smart, handheld devices, there has been a multifold increase in the ability of firms to target and engage with customers through mobile advertising. Therefore, not surprisingly, mobile advertising campaigns have become an integral aspect of firms’ brand building activities, such as improving the awareness and overall visibility of firms' brands. In addition, retailers are increasingly using mobile advertising for targeted promotional activities that increase in-store visits and eventual sales conversions. However, in recent years, mobile or in general online advertising campaigns have been facing one major challenge and one major threat that can negatively impact the …

Counting And Sampling Small Structures In Graph And Hypergraph Data Streams, Themistoklis Haris

Dartmouth College Undergraduate Theses

In this thesis, we explore the problem of approximating the number of elementary substructures called simplices in large k-uniform hypergraphs. The hypergraphs are assumed to be too large to be stored in memory, so we adopt a data stream model, where the hypergraph is defined by a sequence of hyperedges.

First we propose an algorithm that (ε, δ)-estimates the number of simplices using O(m^1+1/k / T) bits of space. In addition, we prove that no constant-pass streaming algorithm can (ε, δ)- approximate the number of simplices using less than O( m ^1+1/k / T ) bits of space. Thus …

Reimagining The Archive For Computational Analysis At Scale, Jamie Rogers

Works of the FIU Libraries

This presentation was part of a three-segment panel discussion sponsored by IS&T, the Society for Imaging Science and Technology, titled "OCR and Text Recognition: Workflows, Trends, and New Applications." This segment covers ways in which we have re-conceptualized archive materials as computationally useful data as well as the value of utilizing data at scale to impact research possibilities. We have been able to accomplish this through an ongoing project "dLOC as Data: A Thematic Approach to Caribbean Newspapers," a collaborative initiative between the Digital Library of the Caribbean, University of Florida, and Florida International University.

A Configurable Social Network For Running Irb-Approved Experiments, Mihovil Mandic

Dartmouth College Undergraduate Theses

Our world has never been more connected, and the size of the social media landscape draws a great deal of attention from academia. However, social networks are also a growing challenge for the Institutional Review Boards concerned with the subjects’ privacy. These networks contain a monumental variety of personal information of almost 4 billion people, allow for precise social profiling, and serve as a primary news source for many users. They are perfect environments for influence operations that are becoming difficult to defend against. Motivated to study online social influence via IRB-approved experiments, we designed and implemented a flexible, scalable, …

Catch You With Cache: Out-Of-Vm Introspection To Trace Malicious Executions, Chao Su, Xuhua Ding, Qinghai Zeng

Research Collection School Of Computing and Information Systems

Out-of-VM introspection is an imperative part of security analysis. The legacy methods either modify the system, introducing enormous overhead, or rely heavily on hardware features, which are neither available nor practical in most cloud environments. In this paper, we propose a novel analysis method, named as Catcher, that utilizes CPU cache to perform out-of-VM introspection. Catcher does not make any modifications to the target program and its running environment, nor demands special hardware support. Implemented upon Linux KVM, it natively introspects the target's virtual memory. More importantly, it uses the cache-based side channel to infer the target control flow. To …

Iquant: Interactive Quantitative Investment Using Sparse Regression Factors, Xuanwu Yue, Qiao Gu, Deyun Wang, Huamin Qu, Yong Wang

Research Collection School Of Computing and Information Systems

The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of “good” factors …

Gpu-Accelerated Graph Label Propagation For Real-Time Fraud Detection, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun

Research Collection School Of Computing and Information Systems

Fraud detection is a pressing challenge for most financial and commercial platforms. In this paper, we study the processing pipeline of fraud detection in a large e-commerce platform of TaoBao. Graph label propagation (LP) is a core component in this pipeline to detect suspicious clusters from the user-interaction graph. Furthermore, the run-time of the LP component occupies 75% overhead of TaoBao’s automated detection pipeline. To enable real-time fraud detection, we propose a GPU-based framework, called GLP, to support large-scale LP workloads in enterprises. We have identified two key challenges when integrating GPU acceleration into TaoBao’s data processing pipeline: (1) programmability …

Cache-Efficient Fork-Processing Patterns On Large Graphs, Shengliang Lu, Shixuan Sun, Johns Paul, Yuchen Li, Bingsheng He

Research Collection School Of Computing and Information Systems

As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. We study the efficiency of handling FPPs in state-of-the-art graph processing systems on multi-core architectures, including Ligra, Gemini, and GraphIt. We find that those systems suffer from severe cache miss penalty because of …

Self-Adaptive Graph Traversal On Gpus, Mo Sha, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

GPU’s massive computing power offers unprecedented opportunities to enable large graph analysis. Existing studies proposed various preprocessing approaches that convert the input graphs into dedicated structures for GPU-based optimizations. However, these dedicated approaches incur significant preprocessing costs as well as weak programmability to build general graph applications. In this paper, we introduce SAGE, a self-adaptive graph traversal on GPUs, which is free from preprocessing and operates on ubiquitous graph representations directly. We propose Tiled Partitioning and Resident Tile Stealing to fully exploit the computing power of GPUs in a runtime and self-adaptive manner. We also propose Sampling-based Reordering to further …

Marrying Top-K With Skyline Queries: Relaxing The Preference Input While Producing Output Of Controllable Size, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically, using a top-�� query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …

Efficient Conditional Gan Transfer With Knowledge Propagation Across Classes, Shahbazi. Mohamad, Zhiwu Huang, Huang, Danda Pani Paudel, Ajad Chhatkuli, Gool L. Van

Research Collection School Of Computing and Information Systems

Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer …

Reciprocal Transformations For Unsupervised Video Object Segmentation, Sucheng Ren, Wenxi Liu, Yongtuo Liu, Haoxin Chen, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and rely on optical flow to capture the motion cues in videos, but the flow information alone is insufficient to distinguish the primary objects from the background objects that move together. This is because, when the noisy motion features are combined with the appearance features, the localization of the primary objects is …

Discovering Interpretable Latent Space Directions Of Gans Beyond Binary Attributes, Huiting Yang, Liangyu Chai, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He

Research Collection School Of Computing and Information Systems

Generative adversarial networks (GANs) learn to map noise latent vectors to high- fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free- form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions …

Minimizing The Regret Of An Influence Provider, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Baihua Zheng

Research Collection School Of Computing and Information Systems

Influence maximization has been studied extensively from the perspective of the influencer. However, the influencer typically purchases influence from a provider, for example in the form of purchased advertising. In this paper, we study the problem from the perspective of the influence provider. Specifically, we focus on influence providers who sell Out-of-Home (OOH) advertising on billboards. Given a set of requests from influencers, how should an influence provider allocate resources to minimize regret, whether due to forgone revenue from influencers whose needs were not met or due to over-provisioning of resources to meet the needs of influencers? We formalize this …

Multi-View Collaborative Network Embedding, Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiaoli Li

Research Collection School Of Computing and Information Systems

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose Multi-view collAborative Network Embedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables …

Hierarchical Reinforcement Learning: A Comprehensive Survey, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek

Research Collection School Of Computing and Information Systems

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future …

On Predicting Personal Values Of Social Media Users Using Community-Specific Language Features And Personal Value Correlation, Amila Silva, Pei Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Personal values have significant influence on individuals’ behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users’ personal values, and developing effective models to predict their personal …

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) …

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 �� ⊆ �� …