Activity Based Traffic Indicator System For Monitoring The Covid-19 Pandemic,
2021
Ateneo de Manila University
Activity Based Traffic Indicator System For Monitoring The Covid-19 Pandemic, Justin Junsay, Aaron Joaquin Lebumfacil, Ivan George Tarun, William Emmanuel S. Yu
Department of Information Systems & Computer Science Faculty Publications
This study describes an activity based traffic indicator system to provide information for COVID-19 pandemic management. The activity based traffic indicator system does this by utilizing a social probability model based on the birthday paradox to determine the exposure risk, the probability of meeting someone infected (PoMSI). COVID-19 data, particularly the 7-day moving average of the daily growth rate of cases (7-DMA of DGR) and cumulative confirmed cases of next week covering a period from April to September 2020, were then used to test PoMSI using Pearson correlation to verify whether it can be used as a factor for the …
Single And Differential Morph Attack Detection,
2021
West Virginia University
Single And Differential Morph Attack Detection, Baaria Chaudhary
Graduate Theses, Dissertations, and Problem Reports (ETD)
Face recognition systems operate on the assumption that a person's face serves as the unique link to their identity. In this thesis, we explore the problem of morph attacks, which have become a viable threat to face verification scenarios precisely because of their inherent ability to break this unique link. A morph attack occurs when two people who share similar facial features morph their faces together such that the resulting face image is recognized as either of two contributing individuals. Morphs inherit enough visual features from both individuals that both humans and automatic algorithms confuse them. The contributions of this …
Novel Techniques In Recovering, Embedding, And Enforcing Policies For Control-Flow Integrity,
2021
Singapore Management University
Novel Techniques In Recovering, Embedding, And Enforcing Policies For Control-Flow Integrity, Yan Lin
Dissertations and Theses Collection (Open Access)
Control-Flow Integrity (CFI) is an attractive security property with which most injected and code-reuse attacks can be defeated, including advanced attacking techniques like Return-Oriented Programming (ROP). CFI extracts a control-flow graph (CFG) for a given program and instruments the program to respect the CFG. Specifically, checks are inserted before indirect branch instructions. Before these instructions are executed during runtime, the checks consult the CFG to ensure that the indirect branch is allowed to reach the intended target. Hence, any sort of controlflow hijacking would be prevented. There are three fundamental components in CFI enforcement. The first component is accurately recovering …
Information Architecture For A Chemical Modeling Knowledge Graph,
2021
Virginia Commonwealth University
Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon
Theses and Dissertations
Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …
Interactive Agent-Based Simulation For Experimentation: A Case Study With Cooperatve Game Theory,
2021
Old Dominion University
Interactive Agent-Based Simulation For Experimentation: A Case Study With Cooperatve Game Theory, Andrew J. Collins, Sheida Etemadidavan
Engineering Management & Systems Engineering Faculty Publications
Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior includes many different aspects depending on the scenario considered. The scenario context of this paper is strategic coalition formation, which is traditionally modeled using cooperative game theory, but we use ABMS instead; as such, it needs to be validated. One approach to validation is to compare the recorded behavior of humans to what was observed in our simulation. We suggest that using an interactive simulation is a good approach to collecting the necessary human behavior data because the humans would be playing in precisely the …
A Blockchain-Enabled Model To Enhance Disaster Aids Network Resilience,
2021
Old Dominion University
A Blockchain-Enabled Model To Enhance Disaster Aids Network Resilience, Farinaz Sabz Ali Pour, Paul Niculescu-Mizil Gheorghe
Engineering Management & Systems Engineering Faculty Publications
The disaster area is a true dynamic environment. Lack of accurate information from the affected area create several challenges in distributing the supplies. The success of a disaster response network is based on collaboration, coordination, sovereignty, and equality in relief distribution. Therefore, a trust-based dynamic communication system is required to facilitate the interactions, enhance the knowledge for the relief operation, prioritize, and coordinate the goods distribution. One of the promising innovative technologies is blockchain technology which enables transparent, secure, and real-time information exchange and automation through smart contracts in a distributed technological ecosystem. This study aims to analyze the application …
Blockchain For A Resilient, Efficient, And Effective Supply Chain, Evidence From Cases,
2021
Old Dominion University
Blockchain For A Resilient, Efficient, And Effective Supply Chain, Evidence From Cases, Adrian Gheorghe, Farinaz Sabz Ali Pour, Unal Tatar, Omer Faruk Keskin
Engineering Management & Systems Engineering Faculty Publications
In the modern acquisition, it is unrealistic to consider single entities as producing and delivering a product independently. Acquisitions usually take place through supply networks. Resiliency, efficiency, and effectiveness of supply networks directly contribute to the acquisition system's resiliency, efficiency, and effectiveness. All the involved firms form a part of a supply network essential to producing the product or service. The decision-makers have to look for new methodologies for supply chain management. Blockchain technology introduces new methods of decentralization and delegation of services, which can transform supply chains and result in a more resilient, efficient, and effective supply chain. This …
Predictive Modeling Of Critical Temperatures In Superconducting Materials,
2021
Technological University Dublin
Predictive Modeling Of Critical Temperatures In Superconducting Materials, Markus Hofmann, Natalia Sizochenko
Articles
n this study, we have investigated quantitative relationships between critical temperaturesof superconductive inorganic materials and the basic physicochemical attributes of these materials(also called quantitative structure-property relationships). We demonstrated that one of the mostrecent studies (titled "A data-driven statistical model for predicting the critical temperature of asuperconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reportson models that were based on the dataset that contains 27% of duplicate entries. We aimed todeliver stable models for a properly cleaned dataset using the same modeling techniques (multiplelinear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability ofour best …
Deep Unsupervised Anomaly Detection,
2021
National University of Singapore
Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
Research Collection School Of Computing and Information Systems
This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results …
Smart Contracts: Will Fintech Be The Catalyst For The Next Global Financial Crisis?,
2021
Catena Technologies
Smart Contracts: Will Fintech Be The Catalyst For The Next Global Financial Crisis?, Randall Duran, Paul Griffin
Research Collection School Of Computing and Information Systems
Purpose: This paper aims to examine the risks associated with smart contracts, a disruptive financial technology (FinTech) innovation, and assesses how in the future they could threaten the integrity of the global financial system. Design/methodology/approach: A qualitative approach is used to identify risk factors related to the use of new financial innovations, by examining how over-the-counter (OTC) derivatives contributed to the Global Financial Crisis (GFC) which occurred during 2007 and 2008. Based on this analysis, the potential for similar concerns with smart contracts are evaluated, drawing on the failure of The DAO on the Ethereum blockchain, which involved the loss …
Learning Adl Daily Routines With Spatiotemporal Neural Networks,
2021
Nanyang Technological University
Learning Adl Daily Routines With Spatiotemporal Neural Networks, Shan Gao, Ah-Hwee Tan, Rossi Setchi
Research Collection School Of Computing and Information Systems
The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for …
"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit,
2021
University of South Carolina - Columbia
"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit, Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth
Publications
During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to …
Facial Emotion Recognition With Noisy Multi-Task Annotations,
2021
Singapore Management University
Facial Emotion Recognition With Noisy Multi-Task Annotations, S. Zhang, Zhiwu Huang, D.P. Paudel, Gool L. Van
Research Collection School Of Computing and Information Systems
Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with noisy multitask annotations. For this new problem, we suggest a formulation from the point of joint distribution match view, which aims at learning more reliable correlations among raw facial images and multi-task labels, resulting in the reduction of noise influence. In our formulation, we exploit a new method to enable the emotion prediction and …
Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing,
2021
Singapore Management University
Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …
Sustainability Of Rewards-Based Crowdfunding: A Quasi-Experimental Analysis Of Funding Targets And Backer Satisfaction,
2021
Singapore Management University
Sustainability Of Rewards-Based Crowdfunding: A Quasi-Experimental Analysis Of Funding Targets And Backer Satisfaction, Michael Wessel, Rob Gleasure, Robert John Kauffman
Research Collection School Of Computing and Information Systems
Rewards-based crowdfunding presents an information asymmetry for participants due to the funding mechanism used. Campaign-backers trust creators to complete projects and deliver rewards as outlined prior to the fundraising process, but creators may discover better opportunities as they progress with a project. Despite this, the all-or-nothing (AON) mechanism on crowdfunding platforms incentivizes creators to set meager funding-targets that are easier to achieve but may offer limited slack when creators wish to simultaneously pursue emerging opportunities later in the project. We explore the related issues of how funding targets seem to be selected by the creators, and how dissatisfaction with the …
Identifying Football Conflict Using Soft-Set Theory In Indonesia Super League,
2021
Universiti Malaya
Identifying Football Conflict Using Soft-Set Theory In Indonesia Super League, Kukuh Wahyudin Pratama
Student Works (2020-2029)
There are several mathematical formal models that handle conflict situations and the most popular one is a rough set theory. With the ability to handle vagueness from the conflict data set, rough set theory has been successfully used in many research. This research used an alternative approach as a method to handle conflict situation in Indonesia Super League. This method was implemented on the respondents or agents who were involved with football club management, match inspector, organizing committee, referees, supporters and players. The novelty of the proposed approach is discussed in rough set theory that include decision rules. It is …
A Theoretical Framework For The Adoption Of Cloud Computing In Hospital Information Systems,
2021
Universiti Malaya
A Theoretical Framework For The Adoption Of Cloud Computing In Hospital Information Systems, Shady Gomaa Abdulaziz
Student Works (2020-2029)
Cloud Computing (CC) is becoming a favourable trend in managing digital data. However, there is a low CC adoption rate in Health-Care Institutions (HCIs) than in other industries due to many reasons like awareness, staff, and costs. This research aims to: (1) investigate the CC adoption in the Saudi Health-Care Institutions (SHCIs), (2) determine the factors that influence the CC adoption in selected SHCIs, (3) develop a theoretical framework for the deployment of CC adoption in selected HCIs based on factors determined earlier, and (4) validate a research framework for the adoption of CC in SHCIs. The purpose of this …
Attribute-Aware Pedestrian Detection In A Crowd,
2021
Singapore Management University
Attribute-Aware Pedestrian Detection In A Crowd, Jialiang Zhang, Lixiang Lin, Jianke Zhu, Yang Li, Yun-Chen Chen, Yao Hu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, …
Unsupervised Representation Learning By Predicting Random Distances,
2021
Singapore Management University
Unsupervised Representation Learning By Predicting Random Distances, Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma
Research Collection School Of Computing and Information Systems
Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected …
A Continual Deepfake Detection Benchmark: Dataset, Methods, And Essentials,
2021
ETH Zurich
A Continual Deepfake Detection Benchmark: Dataset, Methods, And Essentials, Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Van Gool Luc
Research Collection School Of Computing and Information Systems
There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. The suggested CDDB designs multiple evaluations on the detection over easy, hard, and long sequence of deepfake tasks, with a set of appropriate measures. In addition, we exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, …
