Physical Sciences and Mathematics Commons^™

Investigating The Spatial Complexity Of Various Pke-Peks Schematics, Jacob Patterson

Rose-Hulman Undergraduate Research Publications

With the advent of cloud storage, people upload all sorts of information to third party servers. However, uploading plaintext does not seem like a good idea for users who wish to keep their data private. Current solutions to this problem in literature involves integrating Public Key Encryption and Public key encryption with keyword search techniques. The intent of this paper is to analyze the spatial complexities of various PKE-PEKS schemes at various levels of security and discuss potential avenues for improvement.

Databrarianship: The Academic Data Librarian In Theory And Practice, Darren Sweeper

Sprague Library Scholarship and Creative Works

No abstract provided.

A System For Detecting Malicious Insider Data Theft In Iaas Cloud Environments, Jason Nikolai, Yong Wang

Faculty Research & Publications

The Cloud Security Alliance lists data theft and insider attacks as critical threats to cloud security. Our work puts forth an approach using a train, monitor, detect pattern which leverages a stateful rule based k-nearest neighbors anomaly detection technique and system state data to detect inside attacker data theft on Infrastructure as a Service (IaaS) nodes. We posit, instantiate, and demonstrate our approach using the Eucalyptus cloud computing infrastructure where we observe a 100 percent detection rate for abnormal login events and data copies to outside systems.

Preliminary Investigation Of Walking Motion Using A Combination Of Image And Signal Processing, Bradley Schneider, Tanvi Banerjee

Kno.e.sis Publications

We present the results of analyzing gait motion in first-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera as well as validated using the ground truth dataset. Our preliminary results indicate that the extraction of activity from the video …

Answering Why-Not And Why Questions On Reverse Top-K Queries, Qing Liu, Yunjun Gao, Gang Chen, Baihua Zheng, Linlin Zhou

Research Collection School Of Computing and Information Systems

Why-not and why questions can be posed by database users to seek clarifications on unexpected query results. Specifically, why-not questions aim to explain why certain expected tuples are absent from the query results, while why questions try to clarify why certain unexpected tuples are present in the query results. This paper systematically explores the why-not and why questions on reverse top-k queries, owing to its importance in multi-criteria decision making. We first formalize why-not questions on reverse top-k queries, which try to include the missing objects in the reverse top-k query results, and then, we propose a unified framework called …

Efficient Online Summarization Of Large-Scale Dynamic Networks, Qiang Qu, Siyuan Liu, Feida Zhu, Christian S. Jensen

Research Collection School Of Computing and Information Systems

Information diffusion in social networks is often characterized by huge participating communities and viral cascades of high dynamicity. To observe, summarize, and understand the evolution of dynamic diffusion processes in an informative and insightful way is a challenge of high practical value. However, few existing studies aim to summarize networks for interesting dynamic patterns. Dynamic networks raise new challenges not found in static settings, including time sensitivity, online interestingness evaluation, and summary traceability, which render existing techniques inadequate. We propose dynamic network summarization to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or …

Careermapper: An Automated Resume Evaluation Tool, Vivian Lai, Kyong Jin Shim, Richard J. Oentaryo, Philips K. Prasetyo, Casey Vu, Ee-Peng Lim, David Lo

Research Collection School Of Computing and Information Systems

The advent of the Web brought about major changes in the way people search for jobs and companies look for suitable candidates. As more employers and recruitment firms turn to the Web for job candidate search, an increasing number of people turn to the Web for uploading and creating their online resumes. Resumes are often the first source of information about candidates and also the first item of evaluation in candidate selection. Thus, it is imperative that resumes are complete, free of errors and well-organized. We present an automated resume evaluation tool called 'CareerMapper'. Our tool is designed to conduct …

Designing A Datawarehousing And Business Analytics Course Using Experiential Learning Pedagogy, Gottipati Swapna, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Experiential learning refers to learning from experience or learning by doing. Universities have explored various forms for implementing experiential learning such as apprenticeships, internships, cooperative education, practicums, service learning, job shadowing, fellowships and community activities. However, very little has been done in systematically trying to integrate experiential learning to the main stream academic curriculum. Over the last two years, at the authors’ university, a new program titled UNI-X was launched to achieve this. Combining academic curriculum with experiential learning pedagogy, provides a challenging environment for students to use their disciplinary knowledge and skills to tackle real world problems and issues …

Pairwise Relation Classification With Mirror Instances And A Combined Convolutional Neural Network, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on two widely used datasets: SemEval-2010 Task 8 and ACE-2005. The empirical results show that our combined model together …

Unsupervised Feature Selection For Outlier Detection By Modelling Hierarchical Value-Feature Couplings, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu

Research Collection School Of Computing and Information Systems

Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework (CUFS for short) to filter out noisy or redundant features for subsequent outlier detection in categorical data. CUFS quantifies the outlierness (or relevance) of features by learning and integrating both the feature value couplings and feature couplings. Such value-to-feature couplings capture intrinsic data characteristics and …

Iterated Random Oracle: A Universal Approach For Finding Loss In Security Reduction, Fuchun Guo, Willy Susilo, Yi Mu, Rongmao Chen, Jianchang Lai, Guomin Yang

Research Collection School Of Computing and Information Systems

The indistinguishability security of a public-key cryptosystem can be reduced to a computational hard assumption in the random oracle model, where the solution to a computational hard problem is hidden in one of the adversary’s queries to the random oracle. Usually, there is a finding loss in finding the correct solution from the query set, especially when the decisional variant of the computational problem is also hard. The problem of finding loss must be addressed towards tight(er) reductions under this type. In EUROCRYPT 2008, Cash, Kiltz and Shoup proposed a novel approach using a trapdoor test that can solve the …

Validating Social Media Data For Automatic Persona Generation, Jisun An, Haewoon Kwak, Bernard J Jansen

Research Collection School Of Computing and Information Systems

Using personas during interactive design has considerable potential for product and content development. Unfortunately, personas have typically been a fairly static technique. In this research, we validate an approach for creating personas in real time, based on analysis of actual social media data in an effort to automate the generation of personas. We validate that social media data can be implemented as an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation that produces online digital content. Using the organization's YouTube channel, we collect demographic data, customer interactions, and topical …

Zero++: Harnessing The Power Of Zero Appearances To Detect Anomalies In Large-Scale Data Sets, Guansong Pang, Kai Ming Ting, David Albrecht, Huidong Jin

Research Collection School Of Computing and Information Systems

This paper introduces a new unsupervised anomaly detector called ZERO++ which employs the number of zero appearances in subspaces to detect anomalies in categorical data. It is unique in that it works in regions of subspaces that are not occupied by data; whereas existing methods work in regions occupied by data. ZERO++ examines only a small number of low dimensional subspaces to successfully identify anomalies. Unlike existing frequencybased algorithms, ZERO++ does not involve subspace pattern searching. We show that ZERO++ is better than or comparable with the state-of-the-art anomaly detection methods over a wide range of real-world categorical and numeric …

Cast2face: Assigning Character Names Onto Faces In Movie With Actor-Character Correspondence, Guangyu Gao, Mengdi Xu, Jialie Shen, Huangdong Ma, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Automatically identifying characters in movies has attracted researchers' interest and led to several significant and interesting applications. However, due to the vast variation in character appearance as well as the weakness and ambiguity of available annotation, it is still a challenging problem. In this paper, we investigate this problem with the supervision of actor-character name correspondence provided by the movie cast. Our proposed framework, namely, Cast2Face, is featured by: 1) we restrict the assigned names within the set of character names in the cast; 2) for each character, by using the corresponding actor and movie name as keywords, we retrieve …

Towards Building A Review Recommendation System That Trains Novices By Leveraging The Actions Of Experts, Shilpa Khanal

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find …

Cryptographic Reverse Firewall Via Malleable Smooth Projective Hash Functions, Rongmao Chen, Guomin Yang, Guomin Yang, Willy Susilo, Fuchun Guo, Mingwu Zhang

Research Collection School Of Computing and Information Systems

Motivated by the revelations of Edward Snowden, postSnowden cryptography has become a prominent research direction in recent years. In Eurocrypt 2015, Mironov and Stephens-Davidowitz proposed a novel concept named cryptographic reverse firewall (CRF) which can resist exfiltration of secret information from an arbitrarily compromised machine. In this work, we continue this line of research and present generic CRF constructions for several widely used cryptographic protocols based on a new notion named malleable smooth projective hash function. Our contributions can be summarized as follows. – We introduce the notion of malleable smooth projective hash function, which is an extension of the …

From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan

Research Collection School Of Computing and Information Systems

With the booming popularity of online social networks like Twitter and Weibo, online user footprints are accumulating rapidly on the social web. Simultaneously, the question of how to leverage the large-scale user-generated social media data for personal credit scoring comes into the sight of both researchers and practitioners. It has also become a topic of great importance and growing interest in the P2P lending industry. However, compared with traditional financial data, heterogeneous social data presents both opportunities and challenges for personal credit scoring. In this article, we seek a deep understanding of how to learn users’ credit labels from social …

Large Scale Data Mining For It Service Management, Chunqiu Zeng

FIU Electronic Theses and Dissertations

More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure.

Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automation operation are intertwined. Often the system administrators' roles are limited to helping triage tickets to the processing …

Data Exfiltration Detection And Prevention: Virtually Distributed Pomdps For Practically Safer Networks, Sara Marie Mc Carthy, Arunesh Sinha, Milind Tambe, Pratyusa Manadhata

Research Collection School Of Computing and Information Systems

We address the challenge of detecting and addressing advanced persistent threats (APTs) in a computer network, focusing in particular on the challenge of detecting data exfiltration over Domain Name System (DNS) queries, where existing detection sensors are imperfect and lead to noisy observations about the network’s security state. Data exfiltration over DNS queries involves unauthorized transfer of sensitive data from an organization to a remote adversary through a DNS data tunnel to a malicious web domain. Given the noisy sensors, previous work has illustrated that standard approaches fail to satisfactorily rise to the challenge of detecting exfiltration attempts. Instead, we …

M(2)-Abks: Attribute-Based Multi-Keyword Search Over Encrypted Personal Health Records In Multi-Owner Setting, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Fushan Wei, Zhiquan Liu, Xu An Wang

Research Collection School Of Computing and Information Systems

Online personal health record (PHR) is more inclined to shift data storage and search operations to cloud server so as to enjoy the elastic resources and lessen computational burden in cloud storage. As multiple patients' data is always stored in the cloud server simultaneously, it is a challenge to guarantee the confidentiality of PHR data and allow data users to search encrypted data in an efficient and privacy-preserving way. To this end, we design a secure cryptographic primitive called as attribute-based multi-keyword search over encrypted personal health records in multi-owner setting to support both fine-grained access control and multi-keyword search …

Landmark Reranking For Smart Travel Guide Systems By Combining And Analyzing Diverse Media, Junge Shen, Jialie Shen, Tao Mei, Xinbo Gao

Research Collection School Of Computing and Information Systems

Advanced networking technologies and massive online social media have stimulated a booming growth of travel heterogeneous information in recent years. By employing such information, smart travel guide systems, such as landmark ranking systems, have been proposed to offer diverse online travel services. It is essential for a landmark ranking system to structure, analyze, and search the travel heterogeneous information to produce human-expected results. Therefore, currently the most fundamental yet challenging problems can be concluded: 1) how to fuse heterogeneous tourism information and 2) how to model landmark ranking. In this paper, a novel landmark search system is introduced based on …

Learning Sentence Embeddings With Auxiliary Tasks For Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-the-art methods on five benchmark datasets.

A Method Of Integrating Correlation Structures For A Generalized Recursive Route Choice Model, Tien Mai

Research Collection School Of Computing and Information Systems

We propose a way to estimate a generalized recursive route choice model. The model generalizes other existing recursive models in the literature, i.e., (Fosgerau et al., 2013b; Mai et al., 2015c), while being more flexible since it allows the choice at each stage to be any member of the network multivariate extreme value (network MEV) model (Daly and Bierlaire, 2006). The estimation of the generalized model requires defining a contraction mapping and performing contraction iterations to solve the Bellman’s equation. Given the fact that the contraction mapping is defined based on the choice probability generating functions (CPGF) (Fosgerau et al., …

Media Reinvented, Geoff Tan

Asian Management Insights

The brave new world of digital media.

Hierarchical Visualization Of Video Search Results For Topic-Based Browsing, Yu-Gang Jiang, Jiajun Wang, Qiang Wang, Wei Liu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Existing video search engines return a ranked list of videos for each user query, which is not convenient for browsing the results of query topics that have multiple facets, such as the "early life," "personal life," and "presidency" of a query "Barack Obama." Organizing video search results into semantically structured hierarchies with nodes covering different topic facets can significantly improve the browsing efficiency for such queries. In this paper, we introduce a hierarchical visualization approach for video search result browsing, which can help users quickly understand the multiple facets of a query topic in a very well-organized manner. Given a …

Aspect-Based Helpfulness Prediction For Online Product Reviews, Yinfei Yang, Cen Chen, Forrest Sheng Bao

Research Collection School Of Computing and Information Systems

Product reviews greatly influence purchase decisions in online shopping. A common burden of online shopping is that consumers have to search for the right answers through massive reviews, especially on popular products. Hence, estimating and predicting the helpfulness of reviews become important tasks to directly improve shopping experience. In this paper, we propose a new approach to helpfulness prediction by leveraging aspect analysis of reviews. Our hypothesis is that a helpful review will cover many aspects of a product at different emphasis levels. The first step to tackle this problem is to extract proper aspects. Because related products share common …

Content Sampling, Household Informedness, And The Consumption Of Digital Information Goods, Ai Phuong Hoang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Technology and media are delivering content that is transforming society. Providers must compete for consumer attention to sell their digital information goods effectively. This is challenging, since there is a high level of uncertainty associated with the consumption of such goods. Service providers often use free programming to share product information. We examine the effectiveness of content sampling strategy used for on-demand series dramas, a unique class of entertainment goods. The data were extracted from a large set of household video-on-demand (VoD) viewing records and combined with external data sources. We extended a propensity score matching (PSM) approach to handle …

Asean's Digital Economy, Naveen Menon

Asian Management Insights

Transforming industries, enriching lives and propelling progress.

Connecting India, Sam Pitroda

Asian Management Insights

Great inventions will not be born in the absence of the will and intent to embrace change and solve wideranging societal problems.

Summarization Of Egocentric Videos: A Comprehensive Survey, Ana Garcia Del Molino, Cheston Tan, Joo-Hwee Lim, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The introduction of wearable video cameras (e.g., GoPro) in the consumer market has promoted video life-logging, motivating users to generate large amounts of video data. This increasing flow of first-person video has led to a growing need for automatic video summarization adapted to the characteristics and applications of egocentric video. With this paper, we provide the first comprehensive survey of the techniques used specifically to summarize egocentric videos. We present a framework for first-person view summarization and compare the segmentation methods and selection algorithms used by the related work in the literature. Next, we describe the existing egocentric video datasets …