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Articles 1771 - 1800 of 7256
Full-Text Articles in Computer Sciences
Dbl: Efficient Reachability Queries On Dynamic Graphs, Qiuyi Lyu, Yuchen Li, Bingsheng He, Bin Gong
Dbl: Efficient Reachability Queries On Dynamic Graphs, Qiuyi Lyu, Yuchen Li, Bingsheng He, Bin Gong
Research Collection School Of Computing and Information Systems
Reachability query is a fundamental problem on graphs, which has been extensively studied in academia and industry. Since graphs are subject to frequent updates in many applications, it is essential to support efficient graph updates while offering good performance in reachability queries. Existing solutions compress the original graph with the Directed Acyclic Graph (DAG) and propose efficient query processing and index update techniques. However, they focus on optimizing the scenarios where the Strong Connected Components (SCCs) remain unchanged and have overlooked the prohibitively high cost of the DAG maintenance when SCCs are updated. In this paper, we propose DBL, an …
Towards Efficient Motif-Based Graph Partitioning: An Adaptive Sampling Approach, Shixun Huang, Yuchen Li, Zhifeng Bao, Zhao Li
Towards Efficient Motif-Based Graph Partitioning: An Adaptive Sampling Approach, Shixun Huang, Yuchen Li, Zhifeng Bao, Zhao Li
Research Collection School Of Computing and Information Systems
In this paper, we study the problem of efficient motif-based graph partitioning (MGP). We observe that existing methods require to enumerate all motif instances to compute the exact edge weights for partitioning. However, the enumeration is prohibitively expensive against large graphs. We thus propose a sampling-based MGP (SMGP) framework that employs an unbiased sampling mechanism to efficiently estimate the edge weights while trying to preserve the partitioning quality. To further improve the effectiveness, we propose a novel adaptive sampling framework called SMGP+. SMGP+ iteratively partitions the input graph based on up-to-date estimated edge weights, and adaptively adjusts the sampling distribution …
Dycuckoo: Dynamic Hash Tables On Gpus, Yuchen Li, Qiwei Zhu, Zheng Lyu, Zhongdong Huang, Jianling Sun
Dycuckoo: Dynamic Hash Tables On Gpus, Yuchen Li, Qiwei Zhu, Zheng Lyu, Zhongdong Huang, Jianling Sun
Research Collection School Of Computing and Information Systems
The hash table is a fundamental structure that has been implemented on graphics processing units (GPUs) to accelerate a wide range of analytics workloads. Most existing works have focused on static scenarios and occupy large GPU memory to maximize the insertion efficiency. In many cases, data stored in hash tables get updated dynamically, and existing approaches use unnecessarily large memory resources. One naïve solution is to rebuild a hash table (known as rehashing) whenever it is either filled or mostly empty. However, this approach renders significant overheads for rehashing. In this paper, we propose a novel dynamic cuckoo hash table …
Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung
Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung
Research Collection School Of Computing and Information Systems
News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph …
A Fully Dynamic Algorithm For K-Regret Minimizing Sets, Yanhao Wang, Yuchen Li, Raymond Chi-Wing Wong, Kian-Lee Tan
A Fully Dynamic Algorithm For K-Regret Minimizing Sets, Yanhao Wang, Yuchen Li, Raymond Chi-Wing Wong, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation. The k-regret minimizing set (k-RMS) problem was recently proposed for representative tuple discovery. Specifically, for a large database P of tuples with multiple numerical attributes, the k-RMS problem returns a size-r subset Q of P such that, for any possible ranking function, the score of the top-ranked tuple in Q is not much worse than the score of the kth-ranked tuple in P. Although the k-RMS problem has been extensively studied in the literature, existing methods …
Boundary Precedence Image Inpainting Method Based On Self-Organizing Maps, Haibo Pen, Quan Wang, Zhaoxia Wang
Boundary Precedence Image Inpainting Method Based On Self-Organizing Maps, Haibo Pen, Quan Wang, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
In addition to text data analysis, image analysis is an area that has increasingly gained importance in recent years because more and more image data have spread throughout the internet and real life. As an important segment of image analysis techniques, image restoration has been attracting a lot of researchers’ attention. As one of AI methodologies, Self-organizing Maps (SOMs) have been applied to a great number of useful applications. However, it has rarely been applied to the domain of image restoration. In this paper, we propose a novel image restoration method by leveraging the capability of SOMs, and we name …
Spectral Tensor Train Parameterization Of Deep Learning Layers, A. Obukhov, M. Rakhuba, A. Liniger, Zhiwu Huang, S. Georgoulis, D. Dai, Van Gool L.
Spectral Tensor Train Parameterization Of Deep Learning Layers, A. Obukhov, M. Rakhuba, A. Liniger, Zhiwu Huang, S. Georgoulis, D. Dai, Van Gool L.
Research Collection School Of Computing and Information Systems
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight matrices and identifying redundancy sources in the parameterization. We further apply the Tensor Train (TT) decomposition to the compact SVD components, and propose a non-redundant differentiable parameterization of fixed TT-rank tensor manifolds, termed the Spectral Tensor Train Parameterization (STTP). We …
Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua
Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing the individual dishes in a mixed dish image is important for health related applications, e.g. to calculate the nutrition values of the dish. However, most existing methods that focus on single dish classification are not applicable to the recognition of mixed dish images. The main challenge of mixed dish recognition comes from three aspects: a wide range of dish types, the complex dish combination with severe overlap between different dishes and the large visual variances of same …
Tour: Dynamic Topic And Sentiment Analysis Of User Reviews For Assisting App Release, Tianyi Yang, Cuiyun Gao, Jingya Zang, David Lo, Michael R. Lyu
Tour: Dynamic Topic And Sentiment Analysis Of User Reviews For Assisting App Release, Tianyi Yang, Cuiyun Gao, Jingya Zang, David Lo, Michael R. Lyu
Research Collection School Of Computing and Information Systems
App reviews deliver user opinions and emerging issues (e.g., new bugs) about the app releases. Due to the dynamic nature of app reviews, topics and sentiment of the reviews would change along with app release versions. Although several studies have focused on summarizing user opinions by analyzing user sentiment towards app features, no practical tool is released. The large quantity of reviews and noise words also necessitates an automated tool for monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize emerging app issues …
Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu
Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge.Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance …
Integration Of Professional Certifications With Information Systems Business Analytics Track Curriculum, Kyong Jin Shim, Gottipati Swapna, Yi Meng Lau
Integration Of Professional Certifications With Information Systems Business Analytics Track Curriculum, Kyong Jin Shim, Gottipati Swapna, Yi Meng Lau
Research Collection School Of Computing and Information Systems
In this study, we showcase a design of an undergraduate Business Analytics track that integrates professional certifications from Amazon Web Services, Google, SAS, and Salesforce with core Business Analytics courses in an Information Systems undergraduate degree program. Certifications provide an excellent way for students to attain practical, experiential, and demonstrable skills which increasingly more employers look for in job candidates' portfolios. In close collaboration with industry partners, curriculum designers and faculty in institutions of higher learning can leverage high quality hands-on training materials provided by the certification vendors and align it with the core academic course content. Excellent teaching by …
Cross-Topic Rumor Detection Using Topic-Mixtures, Weijieying Ren, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu
Cross-Topic Rumor Detection Using Topic-Mixtures, Weijieying Ren, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu
Research Collection School Of Computing and Information Systems
There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for …
Iotbox: Sandbox Mining To Prevent Interaction Threats In Iot Systems, Hong Jin Kang, Sheng Qin Sim, David Lo
Iotbox: Sandbox Mining To Prevent Interaction Threats In Iot Systems, Hong Jin Kang, Sheng Qin Sim, David Lo
Research Collection School Of Computing and Information Systems
Internet of Things (IoT) apps provide great convenience but exposes us to new safety threats. Unlike traditional software systems, threats may emerge from the joint behavior of multiple apps. While prior studies use handcrafted safety and security policies to detect these threats, these policies may not anticipate all usages of the devices and apps in a smart home, causing false alarms. In this study, we propose to use the technique of mining sandboxes for securing an IoT environment. After a set of behaviors are analyzed from a bundle of apps and devices, a sandbox is deployed, which enforces that previously …
Escape From An Echo Chamber, Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku
Escape From An Echo Chamber, Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku
Research Collection School Of Computing and Information Systems
An echo chamber effect refers to the phenomena that online users revealed selective exposure and ideological segregation on political issues. Prior studies indicate the connection between the spread of misinformation and online echo chambers. In this paper, to help users escape from an echo chamber, we propose a novel news-analysis platform that provides a panoramic view of stances towards a particular event from different news media sources. Moreover, to help users better recognize the stances of news sources which published these news articles, we adopt a news stance classification model to categorize their stances into “agree”, “disagree”, “discuss”, or “unrelated” …
Homophily Outlier Detection In Non-Iid Categorical Data, Guansong Pang, Longbing Cao, Ling Chen
Homophily Outlier Detection In Non-Iid Categorical Data, Guansong Pang, Longbing Cao, Ling Chen
Research Collection School Of Computing and Information Systems
Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier …
Learning Network-Based Multi-Modal Mobile User Interface Embeddings, Gary Ang, Ee-Peng Lim
Learning Network-Based Multi-Modal Mobile User Interface Embeddings, Gary Ang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Rich multi-modal information - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. UI designs are composed of UI entities supporting different functions which together enable the application. To support effective search and recommendation applications over mobile UIs, we need to be able to learn UI representations that integrate latent semantics. In this paper, we propose a novel unsupervised model - Multi-modal Attention-based Attributed Network Embedding (MAAN) model. MAAN is designed to capture both multi-modal and structural network information. Based on the encoder-decoder framework, MAAN aims to learn UI representations that …
Dram Failure Prediction In Aiops: Empirical Evaluation, Challenges And Opportunities, Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, Yongjun Wang
Dram Failure Prediction In Aiops: Empirical Evaluation, Challenges And Opportunities, Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, Yongjun Wang
Research Collection School Of Computing and Information Systems
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multisource dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multiclass classification task and exhaustively evaluate seven popular/stateof-the-art …
Building And Using Digital Libraries For Etds, Edward A. Fox
Building And Using Digital Libraries For Etds, Edward A. Fox
The Journal of Electronic Theses and Dissertations
Despite the high value of electronic theses and dissertations (ETDs), the global collection has seen limited use. To extend such use, a new approach to building digital libraries (DLs) is needed. Fortunately, recent decades have seen that a vast amount of “gray literature” has become available through a diverse set of institutional repositories as well as regional and national libraries and archives. Most of the works in those collections include ETDs and are often freely available in keeping with the open-access movement, but such access is limited by the services of supporting information systems. As explained through a set of …
Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews
Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews
Masters Theses & Doctoral Dissertations
This single-case mechanism study examined the effects of cryptojacking on Internet of Things (IoT) device performance metrics. Cryptojacking is a cyber-threat that involves stealing the computational resources of devices belonging to others to generate cryptocurrencies. The resources primarily include the processing cycles of devices and the additional electricity needed to power this additional load. The literature surveyed showed that cryptojacking has been gaining in popularity and is now one of the top cyberthreats. Cryptocurrencies offer anyone more freedom and anonymity than dealing with traditional financial institutions which make them especially attractive to cybercriminals. Other reasons for the increasing popularity of …
Realium: Building The Future Of Real Estate On The Blockchain, Demitri Haddad
Realium: Building The Future Of Real Estate On The Blockchain, Demitri Haddad
Undergraduate Honors Theses
This paper discusses the prospective challenges, limitations and opportunities in the real estate sector for blockchain. It outlines the idea of Realium, a financial technology application that aims to assist in the purchase, sale, and legal compliance of real estate assets. For more information see docs.realium.io
Stabilization Of Cultural Innovations Depends On Population Density: Testing An Epidemiological Model Of Cultural Evolution Against A Global Dataset Of Rock Art Sites And Climate-Based Estimates Of Ancient Population Densities, Richard Walker, Anders Eriksson, Camille Ruiz, Taylor Howard Newton, Francesco Casalegno
Stabilization Of Cultural Innovations Depends On Population Density: Testing An Epidemiological Model Of Cultural Evolution Against A Global Dataset Of Rock Art Sites And Climate-Based Estimates Of Ancient Population Densities, Richard Walker, Anders Eriksson, Camille Ruiz, Taylor Howard Newton, Francesco Casalegno
Department of Information Systems & Computer Science Faculty Publications
Demographic models of human cultural evolution have high explanatory potential but weak empirical support. Here we use a global dataset of rock art sites and climate and genetics-based estimates of ancient population densities to test a new model based on epidemiological principles. The model focuses on the process whereby a cultural innovation becomes endemic in a population; predicting that this cannot occur unless population density exceeds a critical threshold. Analysis of the data; using a Bayesian statistical framework; shows that the model has stronger empirical support than a proportional model; where detection is directly proportional to population density; or a …
Mass Incarceration In Nebraska: Data And Historical Analysis Of Inmates From 1980-2020, Anna Krause
Mass Incarceration In Nebraska: Data And Historical Analysis Of Inmates From 1980-2020, Anna Krause
Honors Program: Senior Projects (Public)
This study examines Nebraska Department of Corrections inmate data from 1980-2020, looking specifically at inmate demographics and offense trends. State-of-the-art data analysis is conducted to collect, modify, and visualize the data sources. Inmates are organized by each decade they were incarcerated within. The current active prison population is also examined in their own research group. The demographic and offense trends are compared with previous local and national research. Historical context is given for evolving trends in offenses. Solutions for Nebraska prison overcrowding are presented from various interest groups. This study aims to enlighten all interested Nebraskans on who inhabits their …
A High-Accuracy And Power-Efficient Self-Optimizing Wireless Water Level Monitoring Iot Device For Smart City, Tsun-Kuang Chi, Hsiao-Chi Chen, Shih-Lun Chen, Patricia Angela R. Abu
A High-Accuracy And Power-Efficient Self-Optimizing Wireless Water Level Monitoring Iot Device For Smart City, Tsun-Kuang Chi, Hsiao-Chi Chen, Shih-Lun Chen, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
In this paper; a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance; the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16–0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings …
The Dna Cloud: Is It Alive?, Theodoros Bargiotas
The Dna Cloud: Is It Alive?, Theodoros Bargiotas
LSU Doctoral Dissertations
In this analysis, I will firstly be presenting the current knowledge concerning the materiality of the internet based Cloud, which I will henceforth be referring to as simply the Cloud. For organisation purposes I have created two umbrella categories under which I place the ongoing research in the field. Scholars have been addressing the issue of Cloud materiality through broadly two prisms: sociological materiality and geopolitical materiality. The literature of course deals with the intricacies of the Cloud based on its present ferromagnetic storage functionality. However, developments in synthetic biology have caused private tech companies and University spin-offs to flirt …
Block The Root Takeover: Validating Devices Using Blockchain Protocol, Sharmila Paul
Block The Root Takeover: Validating Devices Using Blockchain Protocol, Sharmila Paul
Masters Theses & Doctoral Dissertations
This study addresses a vulnerability in the trust-based STP protocol that allows malicious users to target an Ethernet LAN with an STP Root-Takeover Attack. This subject is relevant because an STP Root-Takeover attack is a gateway to unauthorized control over the entire network stack of a personal or enterprise network. This study aims to address this problem with a potentially trustless research solution called the STP DApp. The STP DApp is the combination of a kernel /net modification called stpverify and a Hyperledger Fabric blockchain framework in a NodeJS runtime environment in userland. The STP DApp works as an Intrusion …
Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur
Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur
Masters Theses & Doctoral Dissertations
Identity and Access Management (IAM) is in the core of any information systems. Traditional IAM systems manage users, applications, and devices within organizational boundaries, and utilize static intelligence for authentication and access control. Identity federation has helped a lot to deal with boundary limitation, but still limited to static intelligence – users, applications and devices must be under known boundaries. However, today’s IAM requirements are much more complex. Boundaries between enterprise and consumer space, on premises and cloud, personal devices and organization owned devices, and home, work and public places are fading away. These challenges get more complicated for Internet …
A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa
A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa
Masters Theses & Doctoral Dissertations
The Internet of Things (IoT) is an environment of connected physical devices and objects that communicate amongst themselves over the internet. The IoT is based on the notion of always-connected customers, which allows businesses to collect large volumes of customer data to give them a competitive edge. Most of the data collected by these IoT devices include personal information, preferences, and behaviors. However, constant connectivity and sharing of data create security and privacy concerns. Laws and regulations like the General Data Protection Regulation (GDPR) of 2016 ensure that customers are protected by providing privacy and security guidelines to businesses. Data …
Jrevealpeg: A Semi-Blind Jpeg Steganalysis Tool Targeting Current Open-Source Embedding Programs, Charles A. Badami
Jrevealpeg: A Semi-Blind Jpeg Steganalysis Tool Targeting Current Open-Source Embedding Programs, Charles A. Badami
Masters Theses & Doctoral Dissertations
Steganography in computer science refers to the hiding of messages or data within other messages or data; the detection of these hidden messages is called steganalysis. Digital steganography can be used to hide any type of file or data, including text, images, audio, and video inside other text, image, audio, or video data. While steganography can be used to legitimately hide data for non-malicious purposes, it is also frequently used in a malicious manner. This paper proposes JRevealPEG, a software tool written in Python that will aid in the detection of steganography in JPEG images with respect to identifying a …
Choosing Isds As A Major: Predictive Analysis, Sarah Johnson
Choosing Isds As A Major: Predictive Analysis, Sarah Johnson
Honors Capstones
No abstract provided.
Can We Classify Cashless Payment Solution Implementations At The Country Level?, Dennis Ng, Robert J. Kauffman, Paul Robert Griffin
Can We Classify Cashless Payment Solution Implementations At The Country Level?, Dennis Ng, Robert J. Kauffman, Paul Robert Griffin
Research Collection School Of Computing and Information Systems
This research commentary proposes a 3-D implementation classification framework to assist service providers and business leaders in understanding the kinds of contexts in which more or less successful cashless payment solutions are observed at point-of-sale (PoS) settings. Three constructs characterize the framework: the digitalization of the local implementation environment; the relative novelty of a given payment technology solution in a country at a specific point in time; and the development status of the country’s national infrastructure. The framework is motivated by a need to support cross-country research in this domain. We analyze eight country mini-cases based on an eight-facet (2 …