OS and Networks Commons^™

Trust Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, semantic sensor web, etc. As collaborating agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update become critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and secure system for managing trust, to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics …

Extending Description Logic Rules, David Carral Martinez, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Description Logics – the logics underpinning the Web Ontology Language OWL – and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL …

A Multi-Modal Sensing And Communication Platform For Continental-Scale Migratory Bird Tracking, David J. Anthony

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

This thesis presents a novel platform for tracking migratory birds on a continental scale. Cellular technology is used to augment the short-range radios that have traditionally been used in wireless sensor networks. The platform utilizes multiple sensors, including a GPS and solid state compass. By using these sensors, the platform is capable of not only tracking a bird’s migration path, but also provides information on a bird’s behavior during its life-cycle. Testing methodology utilizing simulations and aspect-oriented programming is used to reveal faults in the platform prior to deployment on wild animals. In collaboration with the International Crane Foundation, and …

Perceptions Measurement Of Professional Certifications To Augment Buffalo State College Baccalaureate Technology Programs, As A Representative American Postsecondary Educational Institution, Christopher N. Brown

Industrial Technology Theses

The purpose of this study was to assess, measure, and analyze whether voluntary, nationally-recognized professional certification credentials were important to augment technology programs at Buffalo State College (BSC), as a representative postsecondary baccalaureate degree-granting institution offering technology curricula. Six BSC undergraduate technology programs were evaluated within the scope of this study: 1.) Computer Information Systems; 2.) Electrical Engineering, Electronics; 3.) Electrical Engineering, Smart Grid; 4.) Industrial Technology; 5.) Mechanical Engineering; and 6.) Technology Education. This study considered the following three aspects of the problem: a.) postsecondary technology program enrollment and graduation trends; b.) the value/awareness of professional certifications to employers …

Check Image Processing: Webp Conversion And Micr Scan Android Application, Trevor Bliss

Computer Engineering

As more users favor smartphones over computers for simple tasks, small businesses are constantly exploring mobile options to present to their customers. This write-up documents an Android application designed for a small company, which allows users to send pictures of checks to the company’s servers for processing. The picture is taken with the devices built-in camera and is converted to Google’s new image format, WebP. The company’s server processes the check and returns the check’s MICR code as a response. This application leverages the Android NDK and JNI to use Google’s open source image conversion libraries as well as socket …

Adaptive Radial Basis Function Neural Networks-Based Real Time Harmonics Estimation And Pwm Control For Active Power Filters, Eyad Kh Almaita

Dissertations

With the proliferation of nonlinear loads in the power system, harmonic pollution becomes a serious problem that affects the power quality in both transmission and distribution systems. Active power filters (APF) have been proven to be one of the most successful methods for mitigating harmonics problems. So far, different techniques have been used in harmonics extraction and control of APF to satisfy the fast response and the accuracy required by the APF. Neural networks techniques have been used successfully in different real-time and complex situations. This dissertation demonstrates four main tasks; (i) a novel adaptive radial basis function neural networks …

Efficient Reinforcement Learning In Multiple-Agent Systems And Its Application In Cognitive Radio Networks, Jing Zhang

Dissertations

The objective of reinforcement learning in multiple-agent systems is to find an efficient learning method for the agents to behave optimally. Finding Nash equilibrium has become the common learning target for the optimality. However, finding Nash equilibrium is a PPAD (Polynomial Parity Arguments on Directed graphs)-complete problem. The conventional methods can find Nash equilibrium for some special types of Markov games.

This dissertation proposes a new reinforcement learning algorithm to improve the search efficiency and effectiveness for multiple-agent systems. This algorithm is based on the definition of Nash equilibrium and utilizes the greedy and rational features of the agents. When …

Motivated Learning For The Development Of Autonomous Agents, Janusz A. Starzyk, James T. Graham, Pawel Raif, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically changing environments. In …

Magnesium Object Manager Sandbox, A More Effective Sandbox Method For Windows 7, Martin A. Gilligan

Theses and Dissertations

A major issue in computer security is limiting the affects a program can have on a computer. One way is to place the program into a sandbox, a limited environment. Many attempts have been made to create a sandbox that maintains the usability of a program and effectively limits the effects of the program. Sandboxes that limit the resources programs can access, have succeeded. To test the effectiveness of a sandbox that limits the resources a program can access on Windows 7, the Magnesium Object Manager Sandbox (MOMS) is created. MOMS uses a kernel mode Windows component to monitor and …

Stochastic Analysis Of Horizontal Ip Scanning, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov

Computer Science Faculty Publications

Intrusion Detection Systems (IDS) have become ubiquitous in the defense against virus outbreaks, malicious exploits of OS vulnerabilities, and botnet proliferation. As attackers frequently rely on host scanning for reconnaissance leading to penetration, IDS is often tasked with detecting scans and preventing them. However, it is currently unknown how likely an IDS is to detect a given Internet-wide scan pattern and whether there exist sufficiently fast scan techniques that can remain virtually undetectable at large-scale. To address these questions, we propose a simple analytical model for the window-expiration rules of popular IDS tools (i.e., Snort and Bro) and utilize a …

On Superposition Of Heterogeneous Edge Processes In Dynamic Random Graphs, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov

Computer Science Faculty Publications

This paper builds a generic modeling framework for analyzing the edge-creation process in dynamic random graphs in which nodes continuously alternate between active and inactive states, which represent churn behavior of modern distributed systems. We prove that despite heterogeneity of node lifetimes, different initial out-degree, non-Poisson arrival/failure dynamics, and complex spatial and temporal dependency among creation of both initial and replacement edges, a superposition of edge-arrival processes to a live node under uniform selection converges to a Poisson process when system size becomes sufficiently large. Due to the convoluted dependency and non-renewal nature of various point processes, this result significantly …

Localized Deconvolution: Characterizing Nmr-Based Metabolomics Spectroscopic Data Using Localized High-Throughput Deconvolution, Paul E. Anderson, Ajith H. Ranabahu, Deirdre A. Mahle, Nicholas V. Reo, Michael L. Raymer, Amit P. Sheth, Nicholas J. Delraso

Kno.e.sis Publications

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can lead to drastic quantification errors. Attempts at full spectrum deconvolution have been limited in adoption and development due to the computational resources required. Herein, we develop a novel localized deconvolution algorithm for general purpose quantification of NMR-based metabolomics studies. Localized deconvolution decreases average absolute quantification error by 97% and …

Framework For The Analysis Of Coordination In Crisis Response, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John M. Flach

Kno.e.sis Publications

Social Media play a critical role during crisis events, revealing a natural coordination dynamic. We propose a computational framework guided by social science principles to measure, analyze, and understand coordination among the different types of organizations and actors in crisis response. The analysis informs both the scientific account of cooperative behavior and the design of applications and protocols to support crisis management.

Discovering Fine-Grained Sentiment In Suicide Notes, Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth

Kno.e.sis Publications

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system …

A Semantic Problem Solving Environment For Integrative Parasite Research: Identification Of Intervention Targets For Trypanosoma Cruzi, Priti Parikh, Todd Minning, Vinh Nguyen, Sarasi Lalithsena, Amir H. Asiaee, Satya S. Sahoo, Prashant Doshi, Rick L. Tarleton, Amit P. Sheth

Kno.e.sis Publications

Background: Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites, and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external …

Cognitive Approaches For The Semantic Web, Dedre Gentner, Frank Van Harmelen, Pascal Hitzler, Krzysztof Janowicz, Kai-Uwe Kuhnberger

Computer Science and Engineering Faculty Publications

A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but …

Semantics Of Perception: Towards A Semantic Web Approach To Machine Perception, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

The acts of observation and perception provide the building blocks for all human knowledge (Locke, 1690); they are the processes from which all ideas are born; and the sole bond connecting ourselves to the world around us. Now, with the advent of sensor networks capable of observation, this world may be directly accessible to machines. Missing from this vision, however, is the ability of machines to glean semantics from observation; to apprehend entities from detected qualities; to perceive. The systematic automation of this ability is the focus of machine perception -- the ability of computing machines to sense and interpret …

A Scalable Distributed Syntactic, Semantic And Lexical Language Model, Ming Tan, Wenli Zhou, Lei Zheng, Shaojun Wang

Kno.e.sis Publications

This paper presents an attempt at building a large scale distributed composite language model that is formed by seamlessly integrating an n-gram model, a structured language model, and probabilistic latent semantic analysis under a directed Markov random field paradigm to simultaneously account for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm and a follow-up EM algorithm to improve word prediction power on corpora with up to a billion tokens and stored on a supercomputer. The large scale distributed composite …

Alignment-Based Querying Of Linked Open Data, Amit Krishna Joshi, Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Mariana Damova

Kno.e.sis Publications

The Linked Open Data (LOD) cloud is rapidly becoming the largest interconnected source of structured data on diverse domains. The potential of the LOD cloud is enormous, ranging from solving challenging AI issues such as open domain question answering to automated knowledge discovery. However, due to an inherent distributed nature of LOD and a growing number of ontologies and vocabularies used in LOD datasets, querying over multiple datasets and retrieving LOD data remains a challenging task. In this paper, we propose a novel approach to querying linked data by using alignments for processing queries whose constituent data come from heterogeneous …

Extracting Diverse Sentiment Expressions With Target-Dependent Polarity From Twitter, Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, Amit P. Sheth

Kno.e.sis Publications

This study focuses on automatic extraction of sentiment expressions associated with given targets from Twitter. It addresses one of the key challenges in this work: Wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns.