Systems Architecture Commons^™

Geodesic Merging, Konstantinos Georgatos

Publications and Research

We pursue an account of merging through the use of geodesic semantics, the semantics based on the length of the shortest path on a graph. This approach has been fruitful in other areas of belief change such as revision and update. To this end, we introduce three binary merging operators of propositions defined on the graph of their valuations and we characterize them with a finite set of postulates. We also consider a revision operator defined in the extended language of pairs of propositions. This extension allows us to express all merging operators through the set of revision postulates.

Spatial Data Mining Analytical Environment For Large Scale Geospatial Data, Zhao Yang

University of New Orleans Theses and Dissertations

Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend …

Android Drone: Remote Quadcopter Control With A Phone, Aubrey John Russell

Computer Engineering

The purpose of the “Android Drone” project was to create a quadcopter that can be controlled by user input sent over the phone’s Wi-Fi connection or 4G internet connection. Furthermore, the purpose was also to be able to receive live video feedback over the internet connection, thus making the drone an inexpensive option compared to other, equivalent drones that might cost thousands of dollars. Not only that, but the Android phone also has a host of other useful features that could be utilized by the drone: this includes GPS, pathing, picture taking, data storage, networking and TCP/IP, a Java software …

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

Faculty Research & Publications

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

Context-Sensitive Auto-Sanitization For Php, Jared M. Smith, Richard J. Connor, David P. Cunningham, Kyle G. Bashour, Walter T. Work

Chancellor’s Honors Program Projects

No abstract provided.

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

Research Collection School Of Computing and Information Systems

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

Histogram Of Oriented Phase And Gradient (Hopg) Descriptor For Improved Pedestrian Detection, Hussin Ragb, Vijayan K. Asari

Vijayan K. Asari

This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG). The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more …

Towards Autonomous Behavior Learning Of Non-Player Characters In Games, Shu Feng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Non-Player-Characters (NPCs), as found in computer games, can be modelled as intelligent systems, which serve to improve the interactivity and playability of the games. Although reinforcement learning (RL) has been a promising approach to creating the behavior models of non-player characters (NPC), an initial stage of exploration and low performance is typically required. On the other hand, imitative learning (IL) is an effective approach to pre-building a NPC’s behavior model by observing the opponent’s actions, but learning by imitation limits the agent’s performance to that of its opponents. In view of their complementary strengths, this paper proposes a computational model …

Agora: A Knowledge Marketplace For Machine Learning, Mauro Ribeiro

Electronic Thesis and Dissertation Repository

More and more data are becoming part of people's lives. With the popularization of technologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models being …

Improving The Performance Of Ice Sheet Modeling Through Embedded Simulation, Christopher G. Dufour

Electronic Theses and Dissertations

Understanding the impact of global climate change is a critical concern for society at large. One important piece of the climate puzzle is how large-scale ice sheets, such as those covering Greenland and Antarctica, respond to a warming climate. Given such ice sheets are under constant change, developing models that can accurately capture their dynamics represents a significant challenge to researchers. The problem, however, is properly capturing the dynamics of an ice sheet model requires a high model resolution and simulating these models is intractable even for state-of-the-art supercomputers.

This thesis presents a revolutionary approach to accurately capture ice sheet …

Achieving High Reliability And Efficiency In Maintaining Large-Scale Storage Systems Through Optimal Resource Provisioning And Data Placement, Lipeng Wan

Doctoral Dissertations

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues.

First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience …

A Feasible No-Root Approach On Android, Yao Cheng, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Root is the administrative privilege on Android, which is however inaccessible on stock Android devices. Due to the desire for privileged functionalities and the reluctance of rooting their devices, Android users seek for no-root approaches, which provide users with part of root privileges without rooting their devices. In this paper, we newly discover a feasible no-root approach based on the ADB loopback. To ensure such no-root approach is not misused proactively, we examine its dark side, including privacy leakage via logs and user input inference. Finally, we discuss the solutions and suggestions from different perspectives.

An Adaptability-Driven Model And Tool For Analysis Of Service Profitability, Eng Lieh Ouh, Jarzabek Stan

Research Collection School Of Computing and Information Systems

Profitability of adopting Software-as-a-Service (SaaS) solutions forexisting applications is currently analyzed mostly in informal way. Informalanalysis is unreliable because of the many conflicting factors that affect costs andbenefits of offering applications on the cloud. We propose a quantitative economicmodel for evaluating profitability of migrating to SaaS that enables potentialservice providers to evaluate costs and benefits of various migration strategiesand choices of target service architectures. In previous work, we presented arudimentary conceptual SaaS economic model enumerating factors that have todo with service profitability, and defining qualitative relations among them. Aquantitative economic model presented in this paper extends the conceptualmodel with equations …

Optimizing Main Memory Usage In Modern Computing Systems To Improve Overall System Performance, Daniel Jose Campello

FIU Electronic Theses and Dissertations

Operating Systems use fast, CPU-addressable main memory to maintain an application’s temporary data as anonymous data and to cache copies of persistent data stored in slower block-based storage devices. However, the use of this faster memory comes at a high cost. Therefore, several techniques have been implemented to use main memory more efficiently in the literature. In this dissertation we introduce three distinct approaches to improve overall system performance by optimizing main memory usage.

First, DRAM and host-side caching of file system data are used for speeding up virtual machine performance in today’s virtualized data centers. The clustering of VM …

Sustainable Resource Management For Cloud Data Centers, A. S. M. Hasan Mahmud

FIU Electronic Theses and Dissertations

In recent years, the demand for data center computing has increased significantly due to the growing popularity of cloud applications and Internet-based services. Today's large data centers host hundreds of thousands of servers and the peak power rating of a single data center may even exceed 100MW. The combined electricity consumption of global data centers accounts for about 3% of worldwide production, raising serious concerns about their carbon footprint. The utility providers and governments are consistently pressuring data center operators to reduce their carbon footprint and energy consumption. While these operators (e.g., Apple, Facebook, and Google) have taken steps to …

Prosense, Johnny Favazza Ii, Casey Glasgow, Matt Epperson

Computer Engineering

This project aims to gather advanced data sets from MEMS sensors and GPS and deliver it to the user, who can capitalize on the data. The once negligible half-degree difference of your board barreling down a wave can be recorded from a gyro and exploited for the perfect turn. The exact speed dreaded by longboarders where speed wobbles turn into a road rash can be analysed and consequently avoided. Ascertaining the summit of your flight using combined GPS sensors from the ski ramp allows for the correct timing of tricks. When it comes to pursuing excellence in professional sports, amateur …

Packet Filter Approach To Detect Denial Of Service Attacks, Essa Yahya M Muharish

Electronic Theses, Projects, and Dissertations

Denial of service attacks (DoS) are a common threat to many online services. These attacks aim to overcome the availability of an online service with massive traffic from multiple sources. By spoofing legitimate users, an attacker floods a target system with a high quantity of packets or connections to crash its network resources, bandwidth, equipment, or servers. Packet filtering methods are the most known way to prevent these attacks via identifying and blocking the spoofed attack from reaching its target. In this project, the extent of the DoS attacks problem and attempts to prevent it are explored. The attacks categories …

Learning Natural Language Inference With Lstm, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution …

Exemplar-Driven Top-Down Saliency Detection Via Deep Association, Shengfeng He, Rynson W. H. Lau, Qingxiong Yang

Research Collection School Of Computing and Information Systems

Top-down saliency detection is a knowledge-driven search task. While some previous methods aim to learn this "knowledge" from category-specific data, others transfer existing annotations in a large dataset through appearance matching. In contrast, we propose in this paper a locateby-exemplar strategy. This approach is challenging, as we only use a few exemplars (up to 4) and the appearances among the query object and the exemplars can be very different. To address it, we design a two-stage deep model to learn the intra-class association between the exemplars and query objects. The first stage is for learning object-to-object association, and the second …

Poster: Android Whole-System Control Flow Analysis For Accurate Application Behavior Modeling, Huu Hoang Nguyen

Research Collection School Of Computing and Information Systems

Android, the modern operating system for smartphones, together with its millions of apps, has become an important part of human life. There are many challenges to analyzing them. It is important to model the mobile systems in order to analyze the behaviors of apps accurately. These apps are built on top of interactions with Android systems. We aim to automatically build abstract models of the mobile systems and thus automate the analysis of mobile applications and detect potential issues (e.g., leaking private data, causing unexpected crashes, etc.). The expected results will be the accuracy models of actual various versions of …