Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (3560)
- Wright State University (631)
- Walden University (447)
- New Jersey Institute of Technology (143)
- University of Malaya (131)
-
- University of Nebraska at Omaha (119)
- Old Dominion University (109)
- California State University, San Bernardino (100)
- San Jose State University (89)
- University of Dayton (82)
- MMU Press (74)
- City University of New York (CUNY) (70)
- University of Dar es Salaam (65)
- Air Force Institute of Technology (61)
- University of Nebraska - Lincoln (60)
- University of South Florida (56)
- Kennesaw State University (54)
- Nova Southeastern University (52)
- Technological University Dublin (51)
- University of Arkansas, Fayetteville (46)
- Dakota State University (43)
- Claremont Colleges (42)
- California Polytechnic State University, San Luis Obispo (41)
- Institute of Business Administration (38)
- Western Kentucky University (36)
- Purdue University (35)
- Ateneo de Manila University (34)
- Governors State University (34)
- Portland State University (34)
- University of Arkansas Little Rock (33)
- Keyword
-
- Machine learning (123)
- Information technology (91)
- Data mining (90)
- Social media (84)
- Machine Learning (71)
-
- Cybersecurity (63)
- Deep learning (61)
- Twitter (61)
- Artificial intelligence (58)
- Semantic Web (53)
- Online learning (52)
- Databases (46)
- Cloud computing (45)
- Deep Learning (45)
- Information Technology (45)
- Information retrieval (45)
- Classification (44)
- Blockchain (42)
- Database (42)
- Natural language processing (41)
- Ontology (41)
- Big data (40)
- Technology (40)
- Security (39)
- Computer science (38)
- Privacy (38)
- Algorithms (37)
- Clustering (37)
- Information systems (37)
- Management (37)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (3441)
- Kno.e.sis Publications (540)
- Walden Dissertations and Doctoral Studies (447)
- Theses and Dissertations (129)
- Student Works (2000-2009) (120)
-
- Dissertations (114)
- Computer Science Faculty Publications (95)
- Computer Science and Engineering Faculty Publications (91)
- Theses Digitization Project (86)
- Journal of Informatics and Web Engineering (74)
- Master's Projects (68)
- Information Systems and Quantitative Analysis Faculty Proceedings & Presentations (64)
- Tanzania Journal of Engineering and Technology (TJET) (62)
- Dissertations and Theses Collection (Open Access) (58)
- USF Tampa Graduate Theses and Dissertations (51)
- Theses (48)
- CCAC Theses and Dissertations (43)
- Information Systems and Quantitative Analysis Faculty Publications (41)
- CGU Faculty Publications and Research (37)
- International Conference on Information and Communication Technologies (36)
- Open Educational Resources (35)
- Graduate Theses and Dissertations (34)
- Department of Information Systems & Computer Science Faculty Publications (33)
- All Capstone Projects (32)
- Masters Theses & Doctoral Dissertations (32)
- Conference papers (28)
- All Maxine Goodman Levin School of Urban Affairs Publications (27)
- UBT International Conference (23)
- Electronic Theses and Dissertations (22)
- Faculty Articles (22)
- Publication Type
- File Type
Articles 3361 - 3390 of 7334
Full-Text Articles in Computer Sciences
Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim
Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …
Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw
Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: …
Understanding The Information-Based Transformation Of Strategy And Society, Eric K. Clemons, Rajiv M. Dewan, Robert J. Kauffman, Thomas A. Weber
Understanding The Information-Based Transformation Of Strategy And Society, Eric K. Clemons, Rajiv M. Dewan, Robert J. Kauffman, Thomas A. Weber
Research Collection School Of Computing and Information Systems
The world economy is undergoing dramatic changes, largely driven by the new availability of fine-grained information. Innovative ways of using data—large and small—have also prompted a rethinking of the boundaries for the combination and use of knowledge. The strategic design of information flows in the economy has the upside of higher economic rents and competitive advantage, as well as the downsides of wealth inequality and abuse of power. This has brought a wide range of regulatory challenges. To understand the nature of these sweeping changes, it is important to examine the new ways information is used, and how information flows …
Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao
Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao
Research Collection School Of Computing and Information Systems
Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 …
Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide
Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide
Research Collection School Of Computing and Information Systems
Singapore's "smart city" agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined …
Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada
Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada
Research Collection School Of Computing and Information Systems
Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the …
Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in …
Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng
Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng
Research Collection School Of Computing and Information Systems
This paper explores the causality and responsibility problem (CRP) for the non-answers to probabilistic reverse skyline queries (PRSQ). Towards this, we propose an efficient algorithm called CP to compute the causality and responsibility for the non-answers to PRSQ. CP first finds candidate causes, and then, it performs verification to obtain actual causes with their responsibilities, during which several strategies are used to boost efficiency. Extensive experiments using both real and synthetic data sets demonstrate the effectiveness and efficiency of the presented algorithms.
Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim
Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28% and 56% of …
On The Effectiveness Of Virtualization Based Memory Isolation On Multicore Platforms, Siqi Zhao, Xuhua Ding
On The Effectiveness Of Virtualization Based Memory Isolation On Multicore Platforms, Siqi Zhao, Xuhua Ding
Research Collection School Of Computing and Information Systems
Virtualization based memory isolation has beenwidely used as a security primitive in many security systems.This paper firstly provides an in-depth analysis of itseffectiveness in the multicore setting; a first in the literature.Our study reveals that memory isolation by itself is inadequatefor security. Due to the fundamental design choices inhardware, it faces several challenging issues including pagetable maintenance, address mapping validation and threadidentification. As demonstrated by our attacks implementedon XMHF and BitVisor, these issues undermine the security ofmemory isolation. Next, we propose a new isolation approachthat is immune to the aforementioned problems. In our design,the hypervisor constructs a fully isolated micro …
Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim
Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study …
Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang
Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. We propose an end-to-end neural architecture for the task. The architecture is based on match-LSTM, a model we proposed previously for textual entailment, and Pointer Net, a sequence-to-sequence …
Harnessing Legal Complexity, Daniel Katz, J. Ruhl, M Bommarito
Harnessing Legal Complexity, Daniel Katz, J. Ruhl, M Bommarito
All Faculty Scholarship
No abstract provided.
An Evidence-Based Review Of Academic Web Search Engines, 2014-2016: Implications For Librarians’ Practice And Research Agenda, Jody C. Fagan
An Evidence-Based Review Of Academic Web Search Engines, 2014-2016: Implications For Librarians’ Practice And Research Agenda, Jody C. Fagan
Libraries
Academic web search engines have become central to scholarly research. While the fitness of Google Scholar for research purposes has been examined repeatedly, Microsoft Academic and Google Books have not received much attention. Recent studies have much to tell us about the coverage and utility of Google Scholar, its coverage of the sciences, and its utility for evaluating researcher impact. But other aspects have been understudied, such as coverage of the arts and humanities, books, and non-Western, non-English publications. User research has also tapered off. A small number of articles hint at the opportunity for librarians to become expert advisors …
Ten Simple Rules For Responsible Big Data Research, Matthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, Frank Pasquale
Ten Simple Rules For Responsible Big Data Research, Matthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, Frank Pasquale
Geography Faculty Publications
No abstract provided.
Forging Blockchains: Spatial Production And Political Economy Of Decentralized Cryptocurrency Code/Spaces, Joe Blankenship
Forging Blockchains: Spatial Production And Political Economy Of Decentralized Cryptocurrency Code/Spaces, Joe Blankenship
USF Tampa Graduate Theses and Dissertations
Cryptocurrencies and blockchains are increasingly used, implemented and adapted for numerous purposes; people and businesses are integrating these technologies into their practices and strategies, creating new political economies and spaces in and of everyday life. This thesis seeks to develop a foundation of geographic theory for the study of spatial production within and surrounding blockchain technologies focusing on acute studies of Bitcoin as cryptocurrency, Ethereum as digital marketplace, and their conditions of possibility as decentralized autonomous organizations. Utilizing concepts from Henri Lefebvre's Production of Space, this thesis situates blockchain technologies within the wider discussion about the political economy of …
Iota Pi Application For Ios, Deborah Newberry
Iota Pi Application For Ios, Deborah Newberry
Computer Science and Software Engineering
Kappa Kappa Psi is a national honorary fraternity for college band members. They meet every Sunday night, and during these meetings they plan events (both internal and external) that aim to meet our goal of making sure that the Cal Poly band programs have their social, financial, material, and educational needs satisfied. This paper details the steps I took to create an application for them to use internally to help ease organizational processes.
Dark Hazard: Large-Scale Discovery Of Unknown Hidden Sensitive Operations In Android Apps, Xiaorui Pan, Xueqiang Wang, Yue Duan, Xiaofeng Wang, Heng Yin
Dark Hazard: Large-Scale Discovery Of Unknown Hidden Sensitive Operations In Android Apps, Xiaorui Pan, Xueqiang Wang, Yue Duan, Xiaofeng Wang, Heng Yin
Research Collection School Of Computing and Information Systems
Hidden sensitive operations (HSO) such as stealing privacy user data upon receiving an SMS message are increasingly utilized by mobile malware and other potentially-harmful apps (PHAs) to evade detection. Identification of such behaviors is hard, due to the challenge in triggering them during an app’s runtime. Current static approaches rely on the trigger conditions or hidden behaviors known beforehand and therefore cannot capture previously unknown HSO activities. Also these techniques tend to be computationally intensive and therefore less suitable for analyzing a large number of apps. As a result, our understanding of real-world HSO today is still limited, not to …
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Research Collection School Of Computing and Information Systems
Given two object sets Q and O , a metric similarity join finds similar object pairs according to a certain criterion. This operation has a wide variety of applications in data cleaning, data mining, to name but a few. However, the rapidly growing volume of data nowadays challenges traditional metric similarity join methods, and thus, a distributed method is required. In this paper, we adopt a popular distributed framework, namely, MapReduce, to support scalable metric similarity joins. To ensure the load balancing, we present two sampling based partition methods. One utilizes the pivot and the space-filling curve mappings to cluster …
Version-Sensitive Mobile App Recommendation, Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua
Version-Sensitive Mobile App Recommendation, Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Being part and parcel of the daily life for billions of people all over the globe, the domain of mobile Applications (Apps) is the fastest growing sector of mobile market today. Users, however, are frequently overwhelmed by the vast number of released Apps and frequently updated versions. Towards this end, we propose a novel version-sensitive mobile App recommendation framework. It is able to recommend appropriate Apps to right users by jointly exploring the version progression and dual-heterogeneous data. It is helpful for alleviating the data sparsity problem caused by version division. As a byproduct, it can be utilized to solve …
Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang
Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
We present a public key encryption scheme for relational databases (PKDE) that allows the owner to control the execution of cross-relation joins on an outsourced server. The scheme allows anyone to deposit encrypted records in a database on the server. Thereafter, the database owner may authorize the server to join any two relations to identify matching records across them, while preventing self-joins that would reveal information on records that are unmatched in the join. The security of our construction is formally proved in the random oracle model based on the computational bilinear Diffie-Hellman assumption. Specifically, before a relation is joined, …
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Research Collection School Of Computing and Information Systems
Finding components with high connectivity is an important problem in component detection with a wide range of applications, e.g., social network analysis, web-page research and bioinformatics. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real applications present needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive and therefore allows overlapping between components. To find k-VCCs, a top-down framework is first developed to find the exact k-VCCs. To further reduce the high computational cost for input networks of large …
Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen
Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen
Research Collection School Of Computing and Information Systems
Fast approximate nearest neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is product quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low-dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors, and thus, inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called sparse product quantization (SPQ) to encoding …
Improving Automated Bug Triaging With Specialized Topic Model, Xin Xia, David Lo, Ying Ding, Jafar M. Al-Kofahi, Tien N. Nguyen, Xinyu Wang
Improving Automated Bug Triaging With Specialized Topic Model, Xin Xia, David Lo, Ying Ding, Jafar M. Al-Kofahi, Tien N. Nguyen, Xinyu Wang
Research Collection School Of Computing and Information Systems
Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the …
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Research Collection School Of Computing and Information Systems
Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …
Social Tag Relevance Learning Via Ranking-Oriented Neighbor Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
Social Tag Relevance Learning Via Ranking-Oriented Neighbor Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
Research Collection School Of Computing and Information Systems
High quality tags play a critical role in applications involving online multimedia search, such as social image annotation, sharing and browsing. However, user-generated tags in real world are often imprecise and incomplete to describe the image contents, which severely degrades the performance of current search systems. To improve the descriptive powers of social tags, a fundamental issue is tag relevance learning, which concerns how to interpret the relevance of a tag with respect to the contents of an image effectively. In this paper, we investigate the problem from a new perspective of learning to rank, and develop a novel approach …
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Research Collection School Of Computing and Information Systems
The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between discrete trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering the pair of most similar subtrajectories, be them parts of the same or of different input trajectories.The identified pair of subtrajectories is called a motif.The adoption of DFD as the similarity measure in motif discovery,although semantically ideal, is hindered by the high computational complexity of DFD calculation. In this paper, we propose a suite …
Intelligent Web Crawler For Semantic Search Engine, Shujia Zhang
Intelligent Web Crawler For Semantic Search Engine, Shujia Zhang
Master's Projects
A Semantic Search Engine (SSE) is a program that produces semantic-oriented concepts from the Internet. A web crawler is the front end of our SSE; its primary goal is to supply important and necessary information to the data analysis component of SSE. The main function of the analysis component is to produce the concepts (moderately frequent finite sequences of keywords) from the input; it uses some variants of TF-IDF as a primary tool to remove stop words. However, it is a very expensive way to filter out stop words using the idea of TF-IDF. The goal of this project is …
Modeling Adoption Dynamics In Social Networks, Minh Duc Luu
Modeling Adoption Dynamics In Social Networks, Minh Duc Luu
Dissertations and Theses Collection
This dissertation studies the modeling of user-item adoption dynamics where an item can be an innovation, a piece of contagious information or a product. By “adoption dynamics” we refer to the process of users making decision choices to adopt items based on a variety of user and item factors. In the context of social networks, “adoption dynamics” is closely related to “item diffusion”. When a user in a social network adopts an item, she may influence her network neighbors to adopt the item. Those neighbors of her who adopt the item then continue to trigger more adoptions. As this progress …
Maximizing The Probability Of Arriving On Time: A Practical Q-Learning Method, Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans Oliehoek, Ulrich Fastenrath
Maximizing The Probability Of Arriving On Time: A Practical Q-Learning Method, Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans Oliehoek, Ulrich Fastenrath
Research Collection School Of Computing and Information Systems
The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. …