Computer Sciences Commons^™

Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu

Research Collection School Of Computing and Information Systems

The popularity of Online To Offline (O2O) service platforms has spurred the need for online task assignment in real-time spatial data, where streams of spatially distributed tasks and workers are matched in real time such that the total number of assigned pairs is maximized. Existing online task assignment models assume that each worker is either assigned a task immediately or waits for a subsequent task at a fixed location once she/he appears on the platform. Yet in practice a worker may actively move around rather than passively wait in place if no task is assigned. In this paper, we define …

Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu

Research Collection School Of Computing and Information Systems

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize …

Will This Localization Tool Be Effective For This Bug? Mitigating The Impact Of Unreliability Of Information Retrieval Based Bug Localization Tools, Tien-Duy B. Le, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Information retrieval (IR) based bug localization approaches process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recently, several IR-based bug localization tools have been proposed. However, there are no perfect tools that can successfully localize faults within a few number of most suspicious program elements for every single input bug report. Therefore, it is difficult for developers to decide which tool would be effective for a given bug report. Furthermore, for some bug reports, no bug localization tools …

Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision-making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.

Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu

Research Collection School Of Computing and Information Systems

We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …

Secure Encrypted Data Deduplication With Ownership Proof And User Revocation, Wenxiu Ding, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud storage as one of the most important cloud services enables cloud users to save more data without enlarging its own storage. In order to eliminate repeated data and improve the utilization of storage, deduplication is employed to cloud storage. Due to the concern about data security and user privacy, encryption is introduced, but incurs new challenge to cloud data deduplication. Existing work cannot achieve flexible access control and user revocation. Moreover, few of them can support efficient ownership proof, especially public verifiability of ownership. In this paper, we propose a secure encrypted data deduplication scheme with effective ownership proof …

Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu

Research Collection School Of Computing and Information Systems

Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations …

The Simpler The Better: A Unified Approach To Predicting Original Taxi Demands On Large-Scale Online Platforms, Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv

Research Collection School Of Computing and Information Systems

No abstract provided.

Multiplex Media Attention And Disregard Network Among 129 Countries, Haewoon Kwak, Jisun An

Research Collection School Of Computing and Information Systems

We built a multiplex media attention and disregard network (MADN) among 129 countries over 212 days. By characterizing the MADN from multiple levels, we found that it is formed primarily by skewed, hierarchical, and asymmetric relationships. Also, we found strong evidence that our news world is becoming a "global village." However, at the same time, unique attention blocks of the Middle East and North Africa (MENA) region, as well as Russia and its neighbors, still exist.

Encoding And Recall Of Spatio-Temporal Episodic Memory In Real Time, Poo-Hee Chang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Episodic memory enables a cognitive system to improve its performance by reflecting upon past events. In this paper, we propose a computational model called STEM for encoding and recall of episodic events together with the associated contextual information in real time. Based on a class of self-organizing neural networks, STEM is designed to learn memory chunks or cognitive nodes, each encoding a set of co-occurring multi-modal activity patterns across multiple pattern channels. We present algorithms for recall of events based on partial and inexact input patterns. Our empirical results based on a public domain data set show that STEM displays …

Formresnet: Formatted Residual Learning For Image Restoration, Jianbo Jiao, Wei-Chih Tu, Shengfeng He

Research Collection School Of Computing and Information Systems

In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a 'residual formatting layer' to format the residual to …

Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang

Research Collection School Of Computing and Information Systems

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.

Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu

Research Collection School Of Computing and Information Systems

This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors …

Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu

Research Collection School Of Computing and Information Systems

Optimal security reductions for unique signatures (Coron, Eurocrypt 2002) and their generalization, i.e., efficiently re-randomizable signatures (Hofheinz et al. PKC 2012 & Bader et al. Eurocrypt 2016) have been well studied in the literature. Particularly, it has been shown that under a non-interactive hard assumption, any security reduction (with or without random oracles) for a unique signature scheme or an efficiently re-randomizable signature scheme must loose a factor of at least qsqs in the security model of existential unforgeability against chosen-message attacks (EU-CMA), where qsqs denotes the number of signature queries. Note that the number qsqs can be as large …

Bridge Text And Knowledge By Learning Multi-Prototype Entity Mention Embedding, Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li

Research Collection School Of Computing and Information Systems

Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative …

Representativeness-Aware Aspect Analysis For Brand Monitoring In Social Media, Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Ta-Seng Chua

Research Collection School Of Computing and Information Systems

Owing to the fast-responding nature and extreme success of social media, many companies resort to social media sites for monitoring their brands’ reputation and the opinions of general public. To help companies monitor their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ free-text posts in social media. Previous efforts have treated it as a traditional information extraction task, and forgo the specific properties of social media, such as the possible noise in user generated posts and the varying impacts; In contrast, we extract aspects by maximizing their representativeness, which is a …

Personas For Content Creators Via Decomposed Aggregate Audience Statistics, Jisun An, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We propose a novel method for generating personas based on online user data for the increasingly common situation of content creators distributing products via online platforms. We use non-negative matrix factorization to identify user segments and develop personas by adding personality such as names and photos. Our approach can develop accurate personas representing real groups of people using online user data, versus relying on manually gathered data.

Impact Of Artificial Intelligence, Robotics, And Automation On Higher Education, Keng Siau

Research Collection School Of Computing and Information Systems

What is the future of higher education in the AI age? Higher education is expected to be challenged by AI, Robotics, and Automation on multiple fronts. First and foremost, AI, robotics, and automation are replacing and will continue to replace jobs and revolutionalize every nation’s economy and disrupt economic development in the world. Millions of job are expected to be replaced by machines (Zhao & Siau, 2017). Many manufacturing jobs have already been replaced by robots and middle class jobs may be taken over by AI in the near future (Siau & Yang, 2017).The short and long term impact on …

The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although a plethora of studies have been recently conducted on organizational social media, little research has specifically examined how organizational social media can contribute to organizational workers’ performance through knowledge sharing. The objective of this research is to fill this gap by investigating the role of knowledge sharing in organizational social media use and its effect on in-role and innovative performance through the lens of social capital and social cognitive theories. Hypotheses were developed and a survey study is proposed.

Predicting Potential Alzheimer Medical Condition In Elderly Using Iot Sensors - Case Study, Zhi Hao Kevin Chong, Yu Xuan Tee, Ling Jing Toh, Shi Jia Phang, Jie Ying Liew, Bertran Queck, Swapna Gottipati

Research Collection School Of Computing and Information Systems

Ageing population would cause profound problems and the impact is already being felt today in many developed countries such as Singapore. The main concern for the Government is to help the citizens with active ageing through home ownership and good healthcare. With Internet of Things (IoT) gaining traction globally, Singapore is set to take advantage of this technology and leverage it to extend its capabilities towards a graceful Ageing-In-Place for the elderly. This ties in nicely with the expertise of SHINE Seniors project by SMU-iCity Lab, which integrates IT with healthcare in ways that creates innovative IT health solutions that …

Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose …

Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu

Research Collection School Of Computing and Information Systems

Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only …

Improving Pattern Recognition And Neural Network Algorithms With Applications To Solar Panel Energy Optimization, Ernesto Zamora Ramos

UNLV Theses, Dissertations, Professional Papers, and Capstones

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize …

Exact And Heuristic Algorithms For Risk-Aware Stochastic Physical Search, Daniel S. Brown, Jeffrey Hudack, Nathaniel Gemelli, Bikramjit Banerjee

Faculty Publications

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk-aware stochastic physical search (RA-SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task-planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location …

A Scalable Graph-Coarsening Based Index For Dynamic Graph Databases, Akshay Kansal

Boise State University Theses and Dissertations

Graph is a commonly used data structure for modeling complex data such as chemical molecules, images, social networks, and XML documents. This complex data is stored using a set of graphs, known as graph database D. To speed up query answering on graph databases, indexes are commonly used. State-of-the-art graph database indexes do not adapt or scale well to dynamic graph database use; they are static, and their ability to prune possible search responses to meet user needs worsens over time as databases change and grow. Users can re-mine indexes to gain some improvement, but it is time consuming. Users …

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display, Yuan Li

Boise State University Theses and Dissertations

Head mounted displays are characterized by relatively low resolution and low dynamic range. These limitations significantly reduce the visual quality of photo-realistic captures on such displays. This thesis presents an interactive view optimized tone mapping technique for viewing large sized high dynamic range panoramas up to 16384 by 8192 on head mounted displays. This technique generates a separate file storing pre-computed view-adjusted mapping function parameters. We define this technique as ToneTexture. The use of a view adjusted tone mapping allows for expansion of the perceived color space available to the end user. This yields an improved visual appearance of …

Identification Of Unknown Landscape Types Using Cnn Transfer Learning, Ashish Sharma

Boise State University Theses and Dissertations

Unknown image type identification is the problem of identifying unknown types of images from the set of already provided images that are considered to be known, where the known and unknown sets represent different content types. Solving this problem has a lot of security applications such as suspicious object detection during baggage scanning at airport customs, border protection via remote sensing, cancer detection, weather and disaster monitoring, etc. In this thesis, we focus on identification of unknown landscape images. This application has a huge relevance to the context of a smart nation where it can be applied to major national …

Estimating Accuracy Of Personal Identifiable Information In Integrated Data Systems, Amani "Mohammad Jum'h" Amin Shatnawi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Both government agencies and private companies rely on the collection of personal data on an ever-increasing scale. Out of necessity, person data include Personal Identifiable Information (PII), which is information that could potentially identify a specific individual. Many of these data would be integrated, so data analyst, policy makers or corporate officers can use it to make decisions or get a conclusion. Integrating data in a heterogeneous database environment create a need to estimate the accuracy of that data; without a valid assessment of accuracy there is a risk of coming with incorrect conclusions or making bad decision based on …

Database Auto Awesome: Enhancing Database-Centric Web Applications Through Informed Code Generation, Jonathan Adams

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Database Auto Awesome is an approach to enhancing web applications comprised of forms used to interact with stored information. It was inspired by Google's Auto Awesome tool, which provides automatic enhancements for photos. Database Auto Awesome aims to automatically or semi-automatically provide improvements to an application by expanding the functionality of the application and improving the existing code.

This thesis describes a tool that gathers information from the application and provides details on how the parts of the application work together. This information provides the details necessary to generate new portions of an application.

These enhancements are directed by the …

Geometric Facility Location Problems On Uncertain Data, Jingru Zhang

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this dissertation, we study several facility location problems on uncertain data. We mainly consider the k-center problem and many of its variations. These are classical problems in computer science and operations research. These problems on deterministic data have been studied extensively in the literature. We consider them on uncertain data because data in the real world is often associated with uncertainty due to measurement inaccuracy, sampling discrepancy, outdated data sources, resource limitation, etc. Although we focus on the theoretical study, the algorithms developed in this dissertation may find applications in other areas such as data clustering, wireless sensor …