Computer Sciences Commons^™

Dissecting Developer Policy Violating Apps: Characterization And Detection, Su Mon Kywe, Yingjiu Li, Jason Hong, Yao Cheng

Research Collection School Of Computing and Information Systems

To ensure quality and trustworthiness of mobile apps, Google Play store imposes various developer policies. Once an app is reported for exhibiting policy-violating behaviors, it is removed from the store to protect users. Currently, Google Play store relies on mobile users’ feedbacks to identify policy violations. Our paper takes the first step towards understanding these policy-violating apps. First, we crawl 302 Android apps, which are reported in the Reddit forum by mobile users for policy violations and are later removed from the Google Play store. Second, we perform empirical analysis, which reveals that many violating behaviors have not been studied …

Attribute-Based Encryption With Granular Revocation, Hui Cui, Deng, Robert H., Xuhua Ding, Yingjiu Li

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) enables an access control mechanism over encrypted data by specifying access policies over attributes associated with private keys or ciphertexts, which is a promising solution to protect data privacy in cloud storage services. As an encryption system that involves many data users whose attributes might change over time, it is essential to provide a mechanism to selectively revoke data users’ attributes in an ABE system. However, most of the previous revokable ABE schemes consider how to disable revoked data users to access (newly) encrypted data in the system, and there are few of them that can be …

Hydra: Massively Compositional Model For Cross-Project Defect Prediction, Xin Xia, David Lo, Sinno Jialin Pan, Nachiappan Nagappan, Xinyu Wang

Research Collection School Of Computing and Information Systems

Most software defect prediction approaches are trained and applied on data from the same project. However, often a new project does not have enough training data. Cross-project defect prediction, which uses data from other projects to predict defects in a particular project, provides a new perspective to defect prediction. In this work, we propose a HYbrid moDel Reconstruction Approach (HYDRA) for cross-project defect prediction, which includes two phases: genetic algorithm (GA) phase and ensemble learning (EL) phase. These two phases create a massive composition of classifiers. To examine the benefits of HYDRA, we perform experiments on 29 datasets from the …

Attractiveness Versus Competition: Towards An Unified Model For User Visitation, Thanh-Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior provides useful insights about venues as well as the users visiting them. These insights can be used in urban planning and recommender system applications. Unlike previous works that focus on modeling distance effect on user’s choice of check-in venues, this paper studies check-in behaviors affected by two venue-related factors, namely, area attractiveness and neighborhood competitiveness. The former refers to the ability of an area with multiple venues to collectively attract checkins from users, while the latter represents the ability of a venue to compete with its neighbors in the same area for check-ins. We first embark …

Inferring Links Between Concerns And Methods With Multi-Abstraction Vector Space Model, Yun Zhang, David Lo, Xin Xia, Tien-Duy B. Le, Giuseppe Scanniello, Jianling Sun

Research Collection School Of Computing and Information Systems

Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that are relevant to the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose a multi-abstraction concern …

Empirical Study On Synthesis Engines For Semantics-Based Program Repair, Le Dinh Xuan Bach, David Lo, Claire Le Goues

Research Collection School Of Computing and Information Systems

Automatic Program Repair (APR) is an emerging and rapidly growing research area, with many techniques proposed to repair defective software. One notable state-of-the-art line of APR approaches is known as semantics-based techniques, e.g., Angelix, which extract semantics constraints, i.e., specifications, via symbolic execution and test suites, and then generate repairs conforming to these constraints using program synthesis. The repair capability of such approaches-expressive power, output quality, and scalability-naturally depends on the underlying synthesis technique. However, despite recent advances in program synthesis, not much attention has been paid to assess, compare, or leverage the variety of available synthesis engine capabilities in …

Enhancing Automated Program Repair With Deductive Verification, Xuan-Bach D. Le, Quang Loc Le, David Lo, Claire Le Goues

Research Collection School Of Computing and Information Systems

Automated program repair (APR) is a challenging process of detecting bugs, localizing buggy code, generating fix candidates and validating the fixes. Effectiveness of program repair methods relies on the generated fix candidates, and the methods used to traverse the space of generated candidates to search for the best ones. Existing approaches generate fix candidates based on either syntactic searches over source code or semantic analysis of specification, e.g., test cases. In this paper, we propose to combine both syntactic and semantic fix candidates to enhance the search space of APR, and provide a function to effectively traverse the search space. …

Repmatch: Robust Feature Matching And Pose For Reconstructing Modern Cities, Wen-Yan Lin, Siying Liu, Minh N. Do, Ping Tan, Jiangbo Lu

Research Collection School Of Computing and Information Systems

A perennial problem in recovering 3-D models from images is repeated structures common in modern cities. The problem can be traced to the feature matcher which needs to match less distinctive features (permitting wide-baselines and avoiding broken sequences), while simultaneously avoiding incorrect matching of ambiguous repeated features. To meet this need, we develop RepMatch, an epipolar guided (assumes predominately camera motion) feature matcher that accommodates both wide-baselines and repeated structures. RepMatch is based on using RANSAC to guide the training of match consistency curves for differentiating true and false matches. By considering the set of all nearest-neighbor matches, RepMatch can …

Who Is Who In The Mailing List? Comparing Six Disambiguation Heuristics To Identify Multiple Addresses Of A Participant, Igor Scaliante Wiese, José Teodoro Da Silva, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Many software projects adopt mailing lists for the communication of developers and users. Researchers have been mining the history of such lists to study communities' behavior, organization, and evolution. A potential threat of this kind of study is that users often use multiple email addresses to interact in a single mailing list. This can affect the results and tools, when, for example, extracting social networks. This issue is particularly relevant for popular and long-term Open Source Software (OSS) projects, which attract participation of thousands of people. Researchers have proposed heuristics to identify multiple email addresses from the same participant, however …

Bilevel Model-Based Discriminative Dictionary Learning For Recognition, Pan Zhou, Chao Zhang, Lin Zhouchen

Research Collection School Of Computing and Information Systems

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the 0 or 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity …

Inverse Laplace Transform And Post Inversion Formula, Qinmao Zhang

Mathematical Sciences Technical Reports (MSTR)

This paper is dedicated to a general numerical approach to inverse Laplace transforms based on the Post Inversion Formula, which is a theoretical equivalent to the inverse Laplace transform. Though most approaches are too computationally intensive to be of practical use, we introduce an efficient algorithm to compute it based on the Parker-Sochacki method (PSM). This paper also contains some example MATLAB code and algorithm analysis.

Special Issue On Cyberharassment Investigation: Advances And Trends, Joanne Bryce, Virginia N. L. Franqueira, Andrew Marrington

Journal of Digital Forensics, Security and Law

Empirical and anecdotal evidence indicates that cyberharassment is more prevalent as the use of social media becomes increasingly widespread, making geography and physical proximity irrelevant. Cyberharassment can take different forms (e.g., cyberbullying, cyberstalking, cybertrolling), and be motivated by the objectives of inflicting distress, exercising control, impersonation, and defamation. Little is currently known about the modus operandi of offenders and their psychological characteristics. Investigation of these behaviours is particularly challenging because it involves digital evidence distributed across the devices of both alleged offenders and victims, as well as online service providers, sometimes over an extended period of time. This special issue …

Masthead

Journal of Digital Forensics, Security and Law

No abstract provided.

Back Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury

Dissertations, Theses, and Capstone Projects

In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS).

This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one …

Give-Me: Gamification In Virtual Environments For Multimodal Evaluation - A Framework, Wai L. Khoo

Dissertations, Theses, and Capstone Projects

In the last few decades, a variety of assistive technologies (AT) have been developed to improve the quality of life of visually impaired people. These include providing an independent means of travel and thus better access to education and places of work. There is, however, no metric for comparing and benchmarking these technologies, especially multimodal systems. In this dissertation, we propose GIVE-ME: Gamification In Virtual Environments for Multimodal Evaluation, a framework which allows for developers and consumers to assess their technologies in a functional and objective manner. This framework is based on three foundations: multimodality, gamification, and virtual reality. It …

Computerized Classification Of Surface Spikes In Three-Dimensional Electron Microscopic Reconstructions Of Viruses, Younes Benkarroum

Dissertations, Theses, and Capstone Projects

The purpose of this research is to develop computer techniques for improved three-dimensional (3D) reconstruction of viruses from electron microscopic images of them and for the subsequent improved classification of the surface spikes in the resulting reconstruction. The broader impact of such work is the following.

Influenza is an infectious disease caused by rapidly-changing viruses that appear seasonally in the human population. New strains of influenza viruses appear every year, with the potential to cause a serious global pandemic. Two kinds of spikes – hemagglutinin (HA) and neuraminidase (NA) – decorate the surface of the virus particles and these proteins …

The Impact Of Low Self-Control On Online Harassment: Interaction With Opportunity., Hyunin Baek, Michael M. Losavio, George E. Higgins

Journal of Digital Forensics, Security and Law

Developing Internet technology has increased the rates of youth online harassment. This study examines online harassment from adolescents with low self-control and the moderating effect of opportunity. The data used in this study were collected by the Korea Institute of Criminology in 2009. The total sample size was 1,091. The results indicated that low self-control, opportunity, and gender have a significant influence on online harassment. However, these results differed according to gender; for males, low self-control significantly impacted online harassment; for females, however, only low self-control significantly impacted online harassment. Furthermore, the interaction between low self-control and opportunity did not …

Front Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

Table Of Contents

Journal of Digital Forensics, Security and Law

No abstract provided.

A Legal Examination Of Revenge Pornography And Cyber-Harassment, Thomas Lonardo, Tricia Martland, Doug White

Journal of Digital Forensics, Security and Law

This paper examines the current state of the statutes in the United States as they relate to cyber-harassment in the context of "revenge porn". Revenge porn refers to websites which cater to those wishing to exploit, harass, or otherwise antagonize their ex partners using pornographic images and videos which were obtained during their relationships. The paper provide examples and illustrations as well as a summary of current statute in the United States. The paper additionally explores some of the various legal remedies available to victims of revenge pornography.

Differentiating Cyberbullies And Internet Trolls By Personality Characteristics And Self-Esteem, Lauren A. Zezulka, Kathryn C. Seigfried-Spellar

Journal of Digital Forensics, Security and Law

Cyberbullying and internet trolling are both forms of online aggression or cyberharassment; however, research has yet to assess the prevalence of these behaviors in relationship to one another. In addition, the current study was the first to investigate whether individual differences and self-esteem discerned between self-reported cyberbullies and/or internet trolls (i.e., Never engaged in either, Cyberbully-only, Troll-only, Both Cyberbully and Troll). Of 308 respondents solicited from Mechanical Turk, 70 engaged in cyberbullying behaviors, 20 engaged in only trolling behaviors, 129 self-reported both behaviors, and 89 self-reported neither behavior. Results yielded low self-esteem, low conscientiousness, and low internal moral values for …

Toward Online Linguistic Surveillance Of Threatening Messages, Brian H. Spitzberg, Jean Mark Gawron

Journal of Digital Forensics, Security and Law

Threats are communicative acts, but it is not always obvious what they communicate or when they communicate imminent credible and serious risk. This paper proposes a research- and theory-based set of over 20 potential linguistic risk indicators that may discriminate credible from non-credible threats within online threat message corpora. Two prongs are proposed: (1) Using expert and layperson ratings to validate subjective scales in relation to annotated known risk messages, and (2) Using the resulting annotated corpora for automated machine learning with computational linguistic analyses to classify non-threats, false threats, and credible threats. Rating scales are proposed, existing threat corpora …

Network Recovery After Massive Failures, Novella Bartolini, Stefano Ciavarella, Thomas F.La Porta, Simone Silvestri

Computer Science Faculty Research & Creative Works

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a MILP and show that it is NP-hard. We propose a polynomial time heuristic, called Iterative Split and Prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared to several greedy approaches ISPper …

Next Generation Sequencing Data Of A Defined Microbial Mock Community, Esther Singer, Bill Andreopoulos, Robert Bowers, Janey Lee, Shweta Deshpande, Jennifer Chiniquy, Doina Ciobanu, Hans-Peter Klenk, Matthew Zane, Christopher Daum, Alicia Clum, Jan-Fang Cheng, Alex Copeland, Tanja Woyke

Faculty Publications, Computer Science

Generating sequence data of a defined community composed of organisms with complete reference genomes is indispensable for the benchmarking of new genome sequence analysis methods, including assembly and binning tools. Moreover the validation of new sequencing library protocols and platforms to assess critical components such as sequencing errors and biases relies on such datasets. We here report the next generation metagenomic sequence data of a defined mock community (Mock Bacteria ARchaea Community; MBARC-26), composed of 23 bacterial and 3 archaeal strains with finished genomes. These strains span 10 phyla and 14 classes, a range of GC contents, genome sizes, repeat …

Managing A Large Scale Project: Using Strengthsfinder In The Website Redesign, Laura Edwards, Cristina Tofan

EKU Faculty and Staff Scholarship

After doing a library-wide StrengthsFinder assessment that highlighted the strengths of its individuals, EKU Libraries decided to put this strategy into practice by applying it to one of the most complex projects in the life of an academic library: the website redesign. This decentralized approach allowed project managers to align strengths-based teams with phases of the redesign that would most benefit from that team’s unique strengths.

Computer Vision–Based Orthorectification And Georeferencing Of Aerial Image Sets, Mohammadreza Faraji, Xiaojun Qi, Austin Jensen

Computer Science Faculty and Staff Publications

Generating a georeferenced mosaic map from unmanned aerial vehicle (UAV)imagery is a challenging task. Direct and indirect georeferencing methods may fail to generate an accurate mosaic map due to the erroneous exterior orientation parameters stored in the inertial measurement unit (IMU), erroneous global positioning system (GPS) data, and difficulty inlocating ground control points (GCPs) or having a sufficient number of GCPs. This paperpresents a practical framework to orthorectify and georeference aerial images using the robustfeatures-based matching method. The proposed georeferencing process is fully automatic and does not require any GCPs. It is also a near real-time process which can be …

From Damage To Discovery Via Virtual Unwrapping: Reading The Scroll From En-Gedi, W. Brent Seales, Clifford S. Parker, Michael Segal, Emanuel Tov, Pnina Shor, Yosef Porath

Computer Science Faculty Publications

Computer imaging techniques are commonly used to preserve and share readable manuscripts, but capturing writing locked away in ancient, deteriorated documents poses an entirely different challenge. This software pipeline—referred to as “virtual unwrapping”—allows textual artifacts to be read completely and noninvasively. The systematic digital analysis of the extremely fragile En-Gedi scroll (the oldest Pentateuchal scroll in Hebrew outside of the Dead Sea Scrolls) reveals the writing hidden on its untouchable, disintegrating sheets. Our approach for recovering substantial ink-based text from a damaged object results in readable columns at such high quality that serious critical textual analysis can occur. Hence, this …

Landmark Detection With Surprise Saliency Using Convolutional Neural Networks, Feng Tang, Damian Lyons, Daniel Leeds

Faculty Publications

Abstract—Landmarks can be used as reference to enable people or robots to localize themselves or to navigate in their environment. Automatic definition and extraction of appropriate landmarks from the environment has proven to be a challenging task when pre-defined landmarks are not present. We propose a novel computational model of automatic landmark detection from a single image without any pre-defined landmark database. The hypothesis is that if an object looks abnormal due to its atypical scene context (what we call surprise saliency), it then may be considered as a good landmark because it is unique and easy to spot by …

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Dissertations

This thesis reviews the current state of photometric classification in Astronomy and identifies two main gaps: a dependence on handcrafted rules, and a lack of interpretability in the more successful classifiers. To address this, Deep Learning and Computer Vision were used to create a more interpretable model, using unsupervised training to reduce human bias.

The main contribution is the investigation into the impact of using unsupervised feature-extraction from multi-wavelength image data for the classification task. The feature-extraction is achieved by implementing an unsupervised Deep Belief Network to extract lower-dimensionality features from the multi-wavelength image data captured by the Sloan Digital …