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2019

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Articles 31 - 60 of 1250

Full-Text Articles in Physical Sciences and Mathematics

Which Distributions (Or Families Of Distributions) Best Represent Interval Uncertainty: Case Of Permutation-Invariant Criteria, Michael Beer, Julio Urenda, Olga Kosheleva, Vladik Kreinovich Dec 2019

Which Distributions (Or Families Of Distributions) Best Represent Interval Uncertainty: Case Of Permutation-Invariant Criteria, Michael Beer, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we only know the interval containing the quantity of interest, we have no information about the probability of different values within this interval. In contrast to the cases when we know the distributions and can thus use Monte-Carlo simulations, processing such interval uncertainty is difficult -- crudely speaking, because we need to try all possible distributions on this interval. Sometimes, the problem can be simplified: namely, it is possible to select a single distribution (or a small family of distributions) whose analysis provides a good understanding of the situation. The most known case is when we …

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An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop Dec 2019

An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop

Sim Kee Boon Institute for Financial Economics

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) of a linear quantile lasso regression. The FRM is calculated by taking the average …

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Feasibility And Acceptability Of A Rural, Pragmatic, Telemedicine‐ Delivered Healthy Lifestyle Programme, John A. Batsis, Auden C. Mcclure, Aaron B. Weintraub, David F. Kotz, Sivan Rotenberg, Summer B. Cook, Diane Gilbert-Diamond, Kevin Curtis, Courtney J. Stevens, Diane Sette, Richard I. Rothstein Dec 2019

Feasibility And Acceptability Of A Rural, Pragmatic, Telemedicine‐ Delivered Healthy Lifestyle Programme, John A. Batsis, Auden C. Mcclure, Aaron B. Weintraub, David F. Kotz, Sivan Rotenberg, Summer B. Cook, Diane Gilbert-Diamond, Kevin Curtis, Courtney J. Stevens, Diane Sette, Richard I. Rothstein

Dartmouth Scholarship

Background: The public health crisis of obesity leads to increasing morbidity that are even more profound in certain populations such as rural adults. Live, two‐way video‐conferencing is a modality that can potentially surmount geographic barriers and staffing shortages. Methods: Patients from the Dartmouth‐Hitchcock Weight and Wellness Center were recruited into a pragmatic, single‐arm, nonrandomized study of a remotely delivered 16‐week evidence‐based healthy lifestyle programme. Patients were provided hardware and appropriate software allowing for remote participation in all sessions, outside of the clinic setting. Our primary outcomes were feasibility and acceptability of the telemedicine intervention, as well as potential effectiveness on …

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Exploring The State-Of-Receptivity For Mhealth Interventions, Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, Tobias Kowatsch Dec 2019

Exploring The State-Of-Receptivity For Mhealth Interventions, Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, Tobias Kowatsch

Dartmouth Scholarship

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this …

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Why Gamma Distribution Of Seismic Inter-Event Times: A Theoretical Explanation, Laxman Bokati, Aaron A. Velasco, Vladik Kreinovich Dec 2019

Why Gamma Distribution Of Seismic Inter-Event Times: A Theoretical Explanation, Laxman Bokati, Aaron A. Velasco, Vladik Kreinovich

Departmental Technical Reports (CS)

It is known that the distribution of seismic inter-event times is well described by the Gamma distribution. Recently, this fact has been used to successfully predict major seismic events. In this paper, we explain that the Gamma distribution of seismic inter-event times can be naturally derived from the first principles.

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Rating News Claims: Feature Selection And Evaluation, Izzat Alsmadi, Michael J. O'Brien Dec 2019

Rating News Claims: Feature Selection And Evaluation, Izzat Alsmadi, Michael J. O'Brien

Computer Science Faculty Publications

News claims that travel the Internet and online social networks (OSNs) originate from different, sometimes unknown sources, which raises issues related to the credibility of those claims and the drivers behind them. Fact-checking websites such as Snopes, FactCheck, and Emergent use human evaluators to investigate and label news claims, but the process is labor- and time-intensive. Driven by the need to use data analytics and algorithms in assessing the credibility of news claims, we focus on what can be generalized about evaluating human-labeled claims. We developed tools to extract claims from Snopes and Emergent and used public datasets collected by …

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Why Spiking Neural Networks Are Efficient: A Theorem, Michael Beer, Julio Urenda, Olga Kosheleva, Vladik Kreinovich Dec 2019

Why Spiking Neural Networks Are Efficient: A Theorem, Michael Beer, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a theoretical explanation for this empirical …

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Automating Change-Level Self-Admitted Technical Debt Determination, Meng Yan, Xin Xia, Emad Shihab, David Lo, Jianwei Yin, Xiaohu Yang Dec 2019

Automating Change-Level Self-Admitted Technical Debt Determination, Meng Yan, Xin Xia, Emad Shihab, David Lo, Jianwei Yin, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Self-Admitted Technical Debt (SATD) refers to technical debt that is introduced intentionally. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is more beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, and identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. In this paper, we propose a change-level SATD Determination mode by extracting 25 features from software changes that are …

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Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele Dec 2019

Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele

Research Collection School Of Computing and Information Systems

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for …

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Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan Dec 2019

Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective ofTOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes whenvisiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), …

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Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo Dec 2019

Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo

Research Collection School Of Computing and Information Systems

A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method,change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches forrecommending refactorings that change software decomposition (such as a move method) do not explorethe use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First …

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Appmod: Helping Older Adults Manage Mobile Security With Online Social Help, Zhiyuan Wan, Lingfeng Bao, Debin Gao, Eran Toch, Xin Xia, Tamir Mendel, David Lo Dec 2019

Appmod: Helping Older Adults Manage Mobile Security With Online Social Help, Zhiyuan Wan, Lingfeng Bao, Debin Gao, Eran Toch, Xin Xia, Tamir Mendel, David Lo

Research Collection School Of Computing and Information Systems

The rapid adoption of Smartphone devices has caused increasing security and privacy risks and breaches. Catching up with ever-evolving contemporary smartphone technology challenges leads older adults (aged 50+) to reduce or to abandon their use of mobile technology. To tackle this problem, we present AppMoD, a community-based approach that allows delegation of security and privacy decisions a trusted social connection, such as a family member or a close friend. The trusted social connection can assist in the appropriate decision or make it on behalf of the user. We implement the approach as an Android app and describe the results of …

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Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo Dec 2019

Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo

Research Collection School Of Computing and Information Systems

Program comprehension is a fundamental task in software development and maintenance processes. Software developers often need to understand a large amount of existing code before they can develop new features or fix bugs in existing programs. Being able to process programming language code automatically and provide summaries of code functionality accurately can significantly help developers to reduce time spent in code navigation and understanding, and thus increase productivity. Different from natural language articles, source code in programming languages often follows rigid syntactical structures and there can exist dependencies among code elements that are located far away from each other through …

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Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen Dec 2019

Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen

Research Collection School Of Computing and Information Systems

Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …

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Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, Ian Reid Dec 2019

Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, Ian Reid

Research Collection School Of Computing and Information Systems

Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based …

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Guest Editorial: Special Issue On Software Engineering For Mobile Applications, Sebastiano Panichella, Fabio Palomba, David Lo, Meiyappan Nagappan Dec 2019

Guest Editorial: Special Issue On Software Engineering For Mobile Applications, Sebastiano Panichella, Fabio Palomba, David Lo, Meiyappan Nagappan

Research Collection School Of Computing and Information Systems

As Andreessen stated “software is eating the world” (Andreessen 2011). Most of todays industries, from engineering, manufacturing, logistics to health, are run on enterprise software applications and can efficiently automate the analysis and manipulation of several, heterogeneous types of data. One of the most prominent examples of such software diffusion is represented by the widespread adoption of mobile applications. Indeed, during the recent years, the Global App Economy experienced unprecedented growth, driven by the increasing usage of apps and by the greater adoption of mobile devices (e.g., smartphone) around the globe. This mobile application market, which is expected in few …

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Influence, Information And Team Outcomes In Large Scale Software Development, Subhajit Datta Dec 2019

Influence, Information And Team Outcomes In Large Scale Software Development, Subhajit Datta

Research Collection School Of Computing and Information Systems

It is widely perceived that the egalitarian ecosystems of large scale open source software development foster effective team outcomes. In this study, we question this conventional wisdom by examining whether and how the centralization of information and influence in a software development team relate to the quality of the team's work products. Analyzing data from more than a hundred real world projects that include development activities over close to a decade, involving 2000+ developers, who collectively resolve more than two hundred thousand defects through discussions covering more than six hundred thousand comments, we arrive at statistically significant evidence indicating that …

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Punctuation Prediction For Vietnamese Texts Using Conditional Random Fields, Hong Quang Pham, Binh T. Nguyen, Nguyen Viet Cuong Dec 2019

Punctuation Prediction For Vietnamese Texts Using Conditional Random Fields, Hong Quang Pham, Binh T. Nguyen, Nguyen Viet Cuong

Research Collection School Of Computing and Information Systems

We investigate the punctuation prediction for the Vietnamese language. This problem is crucial as it can be used to add suitable punctuation marks to machine-transcribed speeches, which usually do not have such information. Similar to previous works for English and Chinese languages, we formulate this task as a sequence labeling problem. After that, we apply the conditional random field model for solving the problem and propose a set of appropriate features that are useful for prediction. Moreover, we build two corpora from Vietnamese online news and movie subtitles and perform extensive experiments on these data. Finally, we ask four volunteers …

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Twenty Years Of Open Source Software: From Skepticism To Mainstream, Gregorio Robles, Igor Steinmacher, Paul Adams, Christoph Treude Dec 2019

Twenty Years Of Open Source Software: From Skepticism To Mainstream, Gregorio Robles, Igor Steinmacher, Paul Adams, Christoph Treude

Research Collection School Of Computing and Information Systems

Open source software (OSS) has conquered the software world. You can see it nearly everywhere, from Internet infrastructure to mobile phones to the desktop. In addition to that, although many OSS practices were viewed with skepticism 20 years ago, several have become mainstream in software engineering today: from development tools such as Git to practices such as modern code reviews.

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Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa Dec 2019

Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models …

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Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park Dec 2019

Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park

VMASC Publications

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …

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The Dynamic Impacts Of Online Healthcare Community On Physician Altruism: A Hidden Markov Model, Kai Luo, Qiu-Hong Wang, Hock Hai Teo Dec 2019

The Dynamic Impacts Of Online Healthcare Community On Physician Altruism: A Hidden Markov Model, Kai Luo, Qiu-Hong Wang, Hock Hai Teo

Research Collection School Of Computing and Information Systems

Physician altruism is not only a key foundation of modern medical professionalism, but also a critical component in the theoretical health economics study. There is considerable interest in understanding the impacts of contemporary healthcare technology on physician altruism. In this paper, we investigate the dynamic influence of multiple incentive mechanisms developed by an online healthcare community (OHC) on physician altruism. We model physician altruism as the degree of tendency to benefit the patients at the cost of oneself and focus on the incentive mechanisms that give physicians social and economic returns. The dynamics of physician altruism is characterized via a …

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Examining The Theoretical Mechanisms Underlying Health Information Exchange Impact On Healthcare Outcomes: A Physician Agency Perspective, Fang Zhou, Qiu-Hong Wang, Hock Hai Teo Dec 2019

Examining The Theoretical Mechanisms Underlying Health Information Exchange Impact On Healthcare Outcomes: A Physician Agency Perspective, Fang Zhou, Qiu-Hong Wang, Hock Hai Teo

Research Collection School Of Computing and Information Systems

Health information exchange (HIE) is presumed to reduce medical costs by facilitating information sharing across healthcare providers. Existing studies focused on different medical costs or one set of costs, and resulted in mixed findings. We examine the effects of patient access to HIE on two of the most important medical costs of a hospitalization episode - test costs and medication costs - through a natural experiment and the discharge data of a hospital. Besides the negative direct effect of access to HIT on tests costs, we also find its positive spillover effect on medication costs, such that more patients having …

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Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell Dec 2019

Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …

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Quantum Consensus, Jorden Seet, Paul Griffin Dec 2019

Quantum Consensus, Jorden Seet, Paul Griffin

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel consensus mechanism utilizing the quantum properties of qubits. This move from classical computing to quantum computing is shown to theoretically enhance the scalability and speed of distributed consensus as well as improve security and be a potential solution for the problem of blockchain interoperability. Using this method may circumvent the common problem known as the Blockchain Trilemma, enhancing scalability and speed without sacrificing de-centralization or byzantine fault tolerance. Consensus speed and scalability is shown by removing the need for multicast responses and exploiting quantum properties to ensure that only a single multicast is …

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Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque Dec 2019

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

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

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

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Formal Modeling And Analysis Of A Family Of Surgical Robots, Niloofar Mansoor Dec 2019

Formal Modeling And Analysis Of A Family Of Surgical Robots, Niloofar Mansoor

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

Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counterexample showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality and building family-based techniques for both modeling and analysis. This thesis presents a novel approach for building an end to end dependability case for a software product line, where a property is formally modeled, a counterexample is found and then …

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Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang Dec 2019

Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang

Computer Science Faculty Publications

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated …

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Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor Dec 2019

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

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

Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

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Kcrs: A Blockchain-Based Key Compromise Resilient Signature System, Lei Xu, Lin Chen, Zhimin Gao, Xinxin Fan, Kimberly Doan, Shouhuai Xu, Weidong Shi Dec 2019

Kcrs: A Blockchain-Based Key Compromise Resilient Signature System, Lei Xu, Lin Chen, Zhimin Gao, Xinxin Fan, Kimberly Doan, Shouhuai Xu, Weidong Shi

Computer Science Faculty Publications and Presentations

Digital signatures are widely used to assure authenticity and integrity of messages (including blockchain transactions). This assurance is based on assumption that the private signing key is kept secret, which may be exposed or compromised without being detected in the real world. Many schemes have been proposed to mitigate this problem, but most schemes are not compatible with widely used digital signature standards and do not help detect private key exposures. In this paper, we propose a Key Compromise Resilient Signature (KCRS) system, which leverages blockchain to detect key compromises and mitigate the consequences. Our solution keeps a log of …

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