Computer Sciences Commons^™

Projection-Free Distributed Online Learning In Networks, Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang

Research Collection School Of Computing and Information Systems

The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems. However, none of existing studies has explored it in the distributed online learning setting, where locally light computation is assumed. In this paper, we fill this gap by proposing the distributed online conditional gradient algorithm, which eschews the expensive projection operation needed in its counterpart algorithms by exploiting much simpler linear optimization steps. We give a regret bound for the proposed algorithm as a function of the network size and topology, which will be smaller …

Real-Time Influence Maximization On Dynamic Social Streams, Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Influence maximization (IM), which selects a set of k users(called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.Existing IM solutions fail to consider the highly dynamic nature of social influence, which results in either poor seed qualities or long processing time when the network evolves.To address this problem, we define a novel IM query named Stream Influence Maximization (SIM) on social streams.Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over …

A Research Stream On Sentiment Analysis, B. Yuan, Keng Siau

Research Collection School Of Computing and Information Systems

Sentiment analysis (SA) is a powerful mining technique to study online reviews and comments (Lee & Siau, 2001; Adeborna & Siau, 2014; Zhao & Siau, 2017). It is an advanced text mining technique and the goal of SA is to recognize and extract meaningful information from data using natural language processing (NLP) and computational linguistics.SA has been applied to areas such as marketing, online social media, customer service, education, and even energy fields (Yuan & Siau, 2017). For example, SA can be used to identify the attitude of customers according to polarity of the reviews and comments that they left …

Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar

Research Collection School Of Computing and Information Systems

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …

Analyzing The Relationship Between Human Behavior And Indoor Air Quality, Beiyu Lin, Yibo Huangfu, Nathan Lima, Bertram Jobson, Max Kirk, Patrick O’Keeffe, Shelley N. Pressley, Von Walden, Brian Lamb, Diane J. Cook

Computer Science Faculty Publications

In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and …

Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao

Research Collection School Of Computing and Information Systems

Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster …

On Efficiently Finding Reverse K-Nearest Neighbors Over Uncertain Graphs, Yunjun Gao, Xiaoye Miao, Gang Chen, Baihua Zheng, Deng Cai, Huiyong Cui

Research Collection School Of Computing and Information Systems

Reverse k-nearest neighbor (RkNN) query on graphs returns the data objects that take a specified query object q as one of their k-nearest neighbors. It has significant influence in many real-life applications including resource allocation and profile-based marketing. However, to the best of our knowledge, there is little previous work on RkNN search over uncertain graph data, even though many complex networks such as traffic networks and protein–protein interaction networks are often modeled as uncertain graphs. In this paper, we systematically study the problem of reversek-nearest neighbor search on uncertain graphs (UG-RkNN search for short), where graph edges contain uncertainty. …

Basket-Sensitive Personalized Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang

Research Collection School Of Computing and Information Systems

Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the …

Detection Of Comparability Subgraphs From Large Networks, Muthunagai Balakrishnamoorthy

Student Work

Real world large scale networks can be represented as graphs. This approach plays a key role in analysis in the domains of social networks [1] and bioinformatics [2], among others. Analyzing these networks is computationally complex and expensive, especially in terms of memory and time complexity. A popular technique subverting time and computation expense for analyzing networks is extracting substructures, which preserves more important information and less noise [12]. In this work, we use special a special substructure called comparability, which preserves transitive orientation. Our motive is to extract a maximal comparability subgraph since no algorithm exists. Our algorithm is …

Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias, …

Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang

Research Collection School Of Computing and Information Systems

High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agentoperated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a …

Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau

Research Collection School Of Computing and Information Systems

Mental models are organized knowledge structures that individuals form to make sense of the world around them. Cognitive maps are the externalized portrayals of mental models in graphical format. Mental models and cognitive maps have been used as an instructional design method, an assessment tool, and a learning strategy in college education. In this paper, we propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning course materials. Our case study in an Information Systems class illustrates how cognitive maps are constructed from students’ mental models, how learning challenges are identified …

Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together …

Well-Tuned Algorithms For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen, Kun Lu

Research Collection School Of Computing and Information Systems

The Team Orienteering Problem with Time Windows (TOPTW) is the extension of the Orienteering Problem (OP) where each node is limited by a predefined time window during which the service has to start. The objective of the TOPTW is to maximize the total collected score by visiting a set of nodes with a limited number of paths. We propose two algorithms, Iterated Local Search and a hybridization of Simulated Annealing and Iterated Local Search (SAILS), to solve the TOPTW. As indicated in multiple research works on algorithms for the OP and its variants, determining appropriate parameter values in a statistical …

Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a …

Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok

Research Collection School Of Computing and Information Systems

Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps-Ookla Speedtest and MobiPerf-have their RTT measurements inflated. We build three test apps that cover three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete …

Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

Equipped with various sensors and intelligent systems, modern cars turn into entities with connectivity, autonomy, and safety. Car rental/car sharing is an innovative transportation concept and integral in today's urban living. It enables users to access a fleet of vehicles located throughout cities. Complementing public transportation, the car-sharing service helps people to meet their transportation needs economically and in an environmentally responsible manner. When a customer wants to rent a car from a rental company or an owner wants to share a private car with his/her friends or family members, the customer or the user should gain admission to the …

Integrating Apache Spark And R For Big Data Analytics On Solving Geographic Problems, Mengqi Zhang, Tin Seong Kam

Research Collection School Of Computing and Information Systems

With the advent ofdigital technology and smart devices, a flood of digital data is beinggenerated every day. This huge amount of data not only records the historyactivities but also provides future valuable information for organizations andbusinesses. However, the true values of these data will not be fullyappreciated until they have been processed, analyzed and the analysis resultsbeen communicated to decision makers in a business friendly manner.In view of thisneed, big data has been one of the major research focus in the academicresearch community especially in the field of computer science and the softwarevendor as well as the big data service …

Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that …

Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform are build of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing …

Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell

Research Collection School Of Computing and Information Systems

We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different demographic groups. Second, we analyze interview data on social media users in the Middle Eastern region to generate additional insights into the dynamically generated personas. Our findings provide insights into social media users in the Middle East, as well as present a novel methodology of using computational analysis and qualitative …

Implant Treatment In The Predoctoral Clinic: A Retrospective Database Study Of 1091 Patients, Soni Prasad, Christopher Hambrook, Eric Reigle, Katherine Sherman, Naveen K. Bansal, Arthur F. Hefti

Mathematics, Statistics and Computer Science Faculty Research and Publications

Purpose: This retrospective study was conducted at the Marquette University School of Dentistry to (1) characterize the implant patient population in a predoctoral clinic, (2) describe the implants inserted, and (3) provide information on implant failures.

Materials and Methods: The study cohort included 1091 patients who received 1918 dental implants between 2004 and 2012, and had their implants restored by a crown or a fixed dental prosthesis. Data were collected from patient records, entered in a database, and summarized in tables and figures. Contingency tables were prepared and analyzed by a chi-squared test. The cumulative survival probability of implants was …

Enforcing Database Security On Cloud Using A Trusted Third Party Based Model, Victor Fuentes Tello

Graduate Theses and Dissertations

Cloud computing offers a considerable number of advantages to clients and organizations that use several capabilities to store sensitive data, interact with applications, or use technology infrastructure to perform daily activities. The development of new models in cloud computing brings with it a series of elements that must be considered by companies, particularly when the sensitive data needs to be protected. There are some concerns related to security that need to be taken into consideration when a service provider manage and store the data in a location outside the company. In this research, a model that uses a trusted third …

Neutro-Connectedness Theory, Algorithms And Applications, Min Xian

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Connectedness plays an important role in human cognitive and learning activities. Human vision system is very sensitive to the connectedness property of objects and effective in "calculating" the property. The study of connectedness will help us explore the way of human brain extracting the global properties of objects, and enable different avenues to design new artificial intelligent (AI) systems with better performances.

However, connectedness is rarely considered in current AI systems because of the lack of complete theoretic system and efficient computation algorithm. In this work, I focus on building the connectedness theory and algorithms in digital space, and apply …

Recommendation Vs Sentiment Analysis: A Text-Driven Latent Factor Model For Rating Prediction With Cold-Start Awareness, Kaisong Song, Wei Gao, Shi Feng Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang

Research Collection School Of Computing and Information Systems

Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of …

Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng

Research Collection School Of Computing and Information Systems

Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set …

Sequence Aware Functional Encryption And Its Application In Searchable Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Fuchun Guo, Qiong Huang

Research Collection School Of Computing and Information Systems

As a new broad vision of public-key encryption systems, functional encryption provides a promising solution for many challenging security problems such as expressive access control and searching on encrypted data. In this paper, we present two Sequence Aware Function Encryption (SAFE) schemes. Such a scheme is very useful in many forensics applications where the order (or pattern) of the attributes forms an important characteristic of an attribute sequence. Our first scheme supports the matching of two bit strings, while the second scheme can support the matching of general characters. These two schemes are constructed based on the standard Decision Linear …

Fair Deposits Against Double-Spending For Bitcoin Transactions, Xingjie Yu, Shiwen M. Thang, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

In Bitcoin network, the distributed storage of multiple copies of the blockchain opens up possibilities for double spending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. To detect the doublespending and penalize the malicious payer, decentralized non-equivocation contracts have been proposed. The basic idea of these contracts is that the payer locks some coins in a deposit when he initiates a transaction with the payee. If the payer double spends, a cryptographic primitive called accountable assertions can be used to reveal his Bitcoin credentials for the deposit. Thus, the malicious payer could be …

On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao

Research Collection School Of Computing and Information Systems

Android has taken a large share of operating systems forsmart devices including smartphones, and has been an attractive target to theattackers. The arms race between attackers and defenders typically occurs ontwo front lines — the latest attacking technology and the latest updates to theoperating system (including defense mechanisms deployed). In terms of attackingtechnology, Return-Oriented Programming (ROP) is one of the most sophisticatedattack methods on Android devices. In terms of the operating system updates,Android Runtime (ART) was the latest and biggest change to the Android family.In this paper, we investigate the extent to which Android Runtime (ART) makesReturn-Oriented Programming (ROP) attacks …

Seeing Through The Same Lens: Introspecting Guest Address Space At Native Speed, Siqi Zhao, Xuhua Ding, Wen Xu, Dawu Gu

Research Collection School Of Computing and Information Systems

Software-based MMU emulation lies at the heart of out-of-VM live memory introspection, an important technique in the cloud setting that applications such as live forensics and intrusion detection depend on. Due to the emulation, the software-based approach is much slower compared to native memory access by the guest VM. The slowness not only results in undetected transient malicious behavior, but also inconsistent memory view with the guest; both undermine the effectiveness of introspection. We propose the immersive execution environment (ImEE) with which the guest memory is accessed at native speed without any emulation. Meanwhile, the address mappings used within the …