OS and Networks Commons^™

Automatic Identification Of Individual Drugs In Death Certificates, Soon Jye Kho, Amit Sheth, Olivier Bodenreider

Kno.e.sis Publications

Background:

Establishing trends of drug overdoses requires the identification of individual drugs in death certificates, not supported by coding with the International Classification of Diseases. However, identifying drug mentions from the literal portion of death certificates remains challenging due to the variability of drug names.

Objectives:

To automatically identify individual drugs in death certificates.

Methods:

We use RxNorm to collect variants for drug names (generic names, synonyms, brand names) and we algorithmically generate common misspellings. We use this automatically compiled list to identify drug mentions from 703,106 death certificates and compare the performance of our automated approach to that of …

Experimental Investigation On The Effects Of Website Aesthetics On User Performance In Different Virtual Tasks, Meinald T. Thielsch, Russell Haines, Leonie Flacke

Information Technology & Decision Sciences Faculty Publications

In Human-Computer Interaction research, the positive effect of aesthetics on users' subjective impressions and reactions is well-accepted. However, results regarding the influence of interface aesthetics on a user's individual performance as an objective outcome are very mixed, yet of urgent interest due to the proceeding of digitalization. In this web-based experiment (N = 331), the effect of interface aesthetics on individual performance considering three different types of tasks (search, creative, and transfer tasks) is investigated. The tasks were presented on an either aesthetic or unaesthetic website, which differed significantly in subjective aesthetics. Goal orientation (learning versus performance goals) was included …

Empathi: An Ontology For Emergency Managing And Planning About Hazard Crisis, Manas Gaur, Kaeedeh Shekarpour, Amelia Gyrard, Amit P. Sheth

Kno.e.sis Publications

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people.
A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts describing the domain of emergency managing and planning of hazard crises.
Although empathi has a coarse-grained view, it considers the necessary …

Optimal Gateway Placement In Low-Cost Smart Cities, Oluwashina Madamori

Theses and Dissertations--Computer Science

Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of Internet-of-things (IoT) devices to monitor and manage environmental conditions and infrastructure. However, smart city projects can be extremely expensive to deploy and manage partly due to the cost of providing Internet connectivity via 5G or WiFi to IoT devices. This thesis proposes the use of delay tolerant networks (DTNs) as a backbone for smart city communication; enabling developing communities to become smart cities …

Attacker Capability Based Dynamic Deception Model For Large-Scale Networks, Md Ali Reza Al Amin, Sachhin Shetty, Laurent Njilla, Deepak K. Tosh, Charles Kamhoua

Computational Modeling & Simulation Engineering Faculty Publications

In modern days, cyber networks need continuous monitoring to keep the network secure and available to legitimate users. Cyber attackers use reconnaissance mission to collect critical network information and using that information, they make an advanced level cyber-attack plan. To thwart the reconnaissance mission and counterattack plan, the cyber defender needs to come up with a state-of-the-art cyber defense strategy. In this paper, we model a dynamic deception system (DDS) which will not only thwart reconnaissance mission but also steer the attacker towards fake network to achieve a fake goal state. In our model, we also capture the attacker’s capability …

Adaptive Knowledge Networks: A Time Capsule, Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie L. Shalin

Kno.e.sis Publications

❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai

❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events.

❖ Current state-of-the-art curates dynamic knowledge graph from structured text.

❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

A Method Of Evaluation Of High-Performance Computing Batch Schedulers, Jeremy Stephen Futral

UNF Graduate Theses and Dissertations

According to Sterling et al., a batch scheduler, also called workload management, is an application or set of services that provide a method to monitor and manage the flow of work through the system [Sterling01]. The purpose of this research was to develop a method to assess the execution speed of workloads that are submitted to a batch scheduler. While previous research exists, this research is different in that more complex jobs were devised that fully exercised the scheduler with established benchmarks. This research is important because the reduction of latency even if it is miniscule can lead to massive …

Task Relation Networks, Jianshu Li, Pan Zhou, Yunpeng Chen, Jian Zhao, Sujoy Roy, Shuicheng Yan, Jiashi Feng, Terence Sim

Research Collection School Of Computing and Information Systems

Multi-task learning is popular in machine learning and computer vision. In multitask learning, properly modeling task relations is important for boosting the performance of jointly learned tasks. Task covariance modeling has been successfully used to model the relations of tasks but is limited to homogeneous multi-task learning. In this paper, we propose a feature based task relation modeling approach, suitable for both homogeneous and heterogeneous multi-task learning. First, we propose a new metric to quantify the relations between tasks. Based on the quantitative metric, we then develop the task relation layer, which can be combined with any deep learning architecture …

Quantifying Activity Levels Of Community-Dwelling Seniors Through Beacon Monitoring, Jin Qiang Goh, Hwee-Pink Tan, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

The ageing population is rapidly increasing, both in Singapore and worldwide. Due to the shortage of healthcare professionals and institutionalized care, there is a pertinent need for seniors to age-in-place-safely and in the familiarity of their neighborhoods. In addition, changing family structures and rising divorce rates, coupled with the desire for more personal space and independence, have resulted in a significant proportion of seniors who live alone at home. In this paper, we describe a scalable and low-cost monitoring system that can help to identify community-dwelling seniors who are at risk of social isolation and/or frailty. This is achieved by …

Management And Security Of Iot Systems Using Microservices, Tharun Theja Kammara

Master's Projects

Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and …

Tcp Server And Client: Bookstore Enquiry, Fawaz Bukhowa

Student Scholar Symposium Abstracts and Posters

An application called "Bookstore Enquiry", and it is implemented in Java using TCP client-server program. It contains two programs; one program is called "Server" and another one is called "Client". In this application, the 'server' maintains information about books and for each book it stores information like 'BookId', 'BookName', 'BookEdition', 'AvailableStock', 'UnitPrice', 'Discount'. This application works in such a way that, the server runs indefinitely and waits for client requests. The Client will accept the BookId & BookName from console and send it to server. If the server finds any books that matches with sent details, then it shows "BOOK …

Automatic Performance Optimization On Heterogeneous Computer Systems Using Manycore Coprocessors, Chenggang Lai

Graduate Theses and Dissertations

Emerging computer architectures and advanced computing technologies, such as Intel’s Many Integrated Core (MIC) Architecture and graphics processing units (GPU), provide a promising solution to employ parallelism for achieving high performance, scalability and low power consumption. As a result, accelerators have become a crucial part in developing supercomputers. Accelerators usually equip with different types of cores and memory. It will compel application developers to reach challenging performance goals. The added complexity has led to the development of task-based runtime systems, which allow complex computations to be expressed as task graphs, and rely on scheduling algorithms to perform load balancing between …

Efficient Stochastic Gradient Hard Thresholding, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

Research Collection School Of Computing and Information Systems

Stochastic gradient hard thresholding methods have recently been shown to work favorably in solving large-scale empirical risk minimization problems under sparsity or rank constraint. Despite the improved iteration complexity over full gradient methods, the gradient evaluation and hard thresholding complexity of the existing stochastic algorithms usually scales linearly with data size, which could still be expensive when data is huge and the hard thresholding step could be as expensive as singular value decomposition in rank-constrained problems. To address these deficiencies, we propose an efficient hybrid stochastic gradient hard thresholding (HSG-HT) method that can be provably shown to have sample-size-independent gradient …

Reinforcement Learning For Collective Multi-Agent Decision Making, Duc Thien Nguyen

Dissertations and Theses Collection (Open Access)

In this thesis, we study reinforcement learning algorithms to collectively optimize decentralized policy in a large population of autonomous agents. We notice one of the main bottlenecks in large multi-agent system is the size of the joint trajectory of agents which quickly increases with the number of participating agents. Furthermore, the noiseof actions concurrently executed by different agents in a large system makes it difficult for each agent to estimate the value of its own actions, which is well-known as the multi-agent credit assignment problem. We propose a compact representation for multi-agent systems using the aggregate counts to address …

Modeling Movement Decisions In Networks: A Discrete Choice Model Approach, Larry Lin Junjie

Dissertations and Theses Collection (Open Access)

In this dissertation, we address the subject of modeling and simulation of agents and their movement decision in a network environment. We emphasize the development of high quality agent-based simulation models as a prerequisite before utilization of the model as an evaluation tool for various recommender systems and policies. To achieve this, we propose a methodological framework for development of agent-based models, combining approaches such as discrete choice models and data-driven modeling.

The discrete choice model is widely used in the field of transportation, with a distinct utility function (e.g., demand or revenue-driven). Through discrete choice models, the movement decision …

Adaptive Parallelism For Coupled, Multithreaded Message-Passing Programs, Samuel K. Gutiérrez

Computer Science ETDs

Hybrid parallel programming models that combine message passing (MP) and shared- memory multithreading (MT) are becoming more popular, especially with applications requiring higher degrees of parallelism and scalability. Consequently, coupled parallel programs, those built via the integration of independently developed and optimized software libraries linked into a single application, increasingly comprise message-passing libraries with differing preferred degrees of threading, resulting in thread-level heterogeneity. Retroactively matching threading levels between independently developed and maintained libraries is difficult, and the challenge is exacerbated because contemporary middleware services provide only static scheduling policies over entire program executions, necessitating suboptimal, over-subscribed or under-subscribed, configurations. In …

New Insight Into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling And Convexity, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

Research Collection School Of Computing and Information Systems

As an incremental-gradient algorithm, the hybrid stochastic gradient descent (HSGD) enjoys merits of both stochastic and full gradient methods for finite-sum problem optimization. However, the existing rate-of-convergence analysis for HSGD is made under with-replacement sampling (WRS) and is restricted to convex problems. It is not clear whether HSGD still carries these advantages under the common practice of without-replacement sampling (WoRS) for non-convex problems. In this paper, we affirmatively answer this open question by showing that under WoRS and for both convex and non-convex problems, it is still possible for HSGD (with constant step-size) to match full gradient descent in rate …

Authorized Function Homomorphic Signature, Qingwen Guo, Qiong Huang, Guomin Yang

Research Collection School Of Computing and Information Systems

Homomorphic signature (HS) is a novel primitive that allows an agency to carry out arbitrary (polynomial time) computation f on the signed data (m) over right arrow and accordingly gain a signature sigma(h) for the computation result f ((m) over right arrow) with respect to f on behalf of the data owner (DO). However, since DO lacks control of the agency's behavior, receivers would believe that DO did authenticate the computation result even if the agency misbehaves and applies a function that the DO does not want. To address the problem above, in this paper we introduce a new primitive …

Dynamic Hierarchical Cache Management For Cloud Ran And Multi- Access Edge Computing In 5g Networks, Deepika Pathinga Rajendiran

Master's Projects

Cloud Radio Access Networks (CRAN) and Multi-Access Edge Computing (MEC) are two of the many emerging technologies that are proposed for 5G mobile networks. CRAN provides scalability, flexibility, and better resource utilization to support the dramatic increase of Internet of Things (IoT) and mobile devices. MEC aims to provide low latency, high bandwidth and real- time access to radio networks. Cloud architecture is built on top of traditional Radio Access Networks (RAN) to bring the idea of CRAN and in MEC, cloud computing services are brought near users to improve the user’s experiences. A cache is added in both CRAN …

Building Iot Based Applications For Smart Cities: How Can Ontology Catalogs Help?, Amelia Gyrard, Antoine Zimmermann, Amit P. Sheth

Kno.e.sis Publications

The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) …

Using Electronic Health Records To Characterize Prescription Patterns: Focus On Antidepressants In Nonpsychiatric Outpatient Settings, Joseph J. Deferio, Tomer T. Levin, Judith Cukor, Samprit Banerjee, Rozan Abdulrahman, Amit P. Sheth, Neel Mehta, Jyotishman Pathak

Kno.e.sis Publications

Objective

To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods

A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results

Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated …

Poster: Privacy-Preserving Boosting With Random Linear Classifiers, Sagar Sharma, Keke Chen

Kno.e.sis Publications

We propose SecureBoost, a privacy-preserving predictive modeling framework, that allows service providers (SPs) to build powerful boosting models over encrypted or randomly masked user submit- ted data. SecureBoost uses random linear classifiers (RLCs) as the base classifiers. A Cryptographic Service Provider (CSP) manages keys and assists the SP’s processing to reduce the complexity of the protocol constructions. The SP learns only the base models (i.e., RLCs) and the CSP learns only the weights of the base models and a limited leakage function. This separated parameter holding avoids any party from abusing the final model or conducting model-based attacks. We evaluate …

Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao

Research Collection School Of Computing and Information Systems

Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of5 moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt6 to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split7 into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a8 much larger memory space at the server side. In this paper, we present SIMkNN, a scalable and in-memory …

Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of …

Building Test Anonymity Networks In A Cybersecurity Lab Environment, John Schriner

Student Theses

This paper explores current methods for creating test anonymity networks in a laboratory environment for the purpose of improving these networks while protecting user privacy. We first consider how each of these networks is research-driven and interested in helping researchers to conduct their research ethically. We then look to the software currently available for researchers to set up in their labs. Lastly we explore ways in which digital forensics and cybersecurity students could get involved with these projects and look at several class exercises that help students to understand particular attacks on these networks and ways they can help to …

Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results …

Experiences & Challenges With Server-Side Wifi Indoor Localization Using Existing Infrastructure, Dheryta Jaisinghani, Rajesh Krishna Balan, Vinayak Naik, Archan Misra, Youngki Lee

Research Collection School Of Computing and Information Systems

Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. …

Knowledge-Aware Attentive Neural Network For Ranking Question Answer Pairs, Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Research Collection School Of Computing and Information Systems

Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop …

Situation-Aware Authenticated Video Broadcasting Over Train-Trackside Wifi Networks, Yongdong Wu, Dengpan Ye, Zhuo Wei, Qian Wang, William Tan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Live video programmes can bring in better travel experience for subway passengers and earn abundant advertisement revenue for subway operators. However, because the train-trackside channels for video dissemination are easily accessible to anyone, the video traffic are vulnerable to attacks which may cause deadly tragedies. This paper presents a situation-aware authenticated video broadcasting scheme in the railway network which consists of train, on-board sensor, trackside GSM-R (Global System for Mobile Communications-Railway) device, WiFi AP (Access Point), and train control center. Specifically, the scheme has four modules: (1) a train uses its on-board sensors to obtain its speed, location, and RSSI …