Physical Sciences and Mathematics Commons^™

Next Generation Crystal Viewing Tool, Zach Schaffter, Gerhard Klimeck

Gerhard Klimeck

The science and engineering community is limited when it comes to crystal viewing software tools. Each tool lacks in a different area such as customization of structures or visual output. Crystal Viewer 2.0 was created to have all of these features in one program. This one tool simulates virtually any crystal structure with any possible material. The vtkvis widget offers users advanced visual options not seen in any other crystal viewing software. In addition, the powerful engine behind Crystal Viewer 2.0, nanoelectronic modeling 5 or (NEMO5), performs intensive atomic calculations depending on user input. A graphical user interface, or GUI, …

Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito

Ole J Mengshoel

Making Sense Of Software Development And Personality Types, Luiz Fernando Capretz, Faheem Ahmed Dr.

Luiz Fernando Capretz

No abstract provided.

Software Project Risk Assessment And Effort Contingency Model Based On Cost Factors, Luiz Fernando Capretz

Luiz Fernando Capretz

In the early stages of a software development life cycle, effort estimation plays a critical role in helping project managers predict the demands with respect to the budgeting, scheduling, and the allocation of resources. In this situation, the ideal estimation calculation should provide an approximate value figure, which will consist of a base estimation value plus a contingency allowance value, which will cover the risks and assumptions necessary for particular estimation calculations.

However, most software effort estimation methodologies, which include the COCOMO model, provide a fixed effort estimate value instead of an approximate value, and consequently the existing effort estimation …

Towards An Early Software Estimation Using Log-Linear Regression And A Multilayer Perceptron Model, Ali Bou Nassif, Luiz Fernando Capretz

Luiz Fernando Capretz

Software estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. …

Spring­11: Pdc In Cs1/2 And A Mobile/Cloud Intermediate Mobile/Cloud Intermediate Software Design Course, Joseph P. Kaylor, Konstantin Läufer, Chandra N. Sekharan, George K. Thiruvathukal

George K. Thiruvathukal

Recent changes in the environment of Loyola University Chicago’s Department of Computer Science include a better differentiation of our four undergraduate majors, growing interest in computing among science majors, and an increased demand for graduates with mobile and cloud skills. In our continued effort to incorporate parallel and distributed computing topics into the undergraduate curriculum, we are focusing on these three existing courses: CS1: In response to a request from the physics department, we started to offer a CS1 section aimed at majors in physics and other hard sciences this spring semester. This section includes some material on numerical methods …

Network Technologies Used To Aggregate Environmental Data, Paul Stasiuk, Konstantin Läufer, George K. Thiruvathukal

George K. Thiruvathukal

The goal of the Loyola Weather Service (lws) project is to design and build a system of functioning environmental monitoring widgets that can intelligently and autonomously control the environment around them based on set thresholds and triggers. The widgets will also have the ability to aggregate their data and easily display this data in various ways: through a user interface in the room that the widget is placed, via a web application, and programmatically via a RESTful web service.

A Polyglot Approach To Bioinformatics Data Integration: Phylogenetic Analysis Of Hiv-1, Steven Reisman, Catherine Putonti, George K. Thiruvathukal, Konstantin Läufer

George K. Thiruvathukal

RNA-interference has potential therapeutic use against HIV-1 by targeting highly-functional mRNA sequences that contribute to the virulence of the virus. Empirical work has shown that within cell lines, all of the HIV-1 genes are affected by RNAi-induced gene silencing. While promising, inherent in this treatment is the fact that RNAi sequences must be highly specific. HIV, however, mutates rapidly, leading to the evolution of viral escape mutants. In fact, such strains are under strong selection to include mutations within the targeted region, evading the RNAi therapy and thus increasing the virus’ fitness in the host. Taking a phylogenetic approach, we …

Building Capable, Energy-Efficient, Flexible Visualization And Sensing Clusters From Commodity Tablets, Thomas Delgado Dias, Xian Yan, Konstantin Läufer, George K. Thiruvathukal

George K. Thiruvathukal

We explore the application of clusters of commodity tablet devices to problems spanning a “trilogy” of concerns: visualization, sensing, and computation. We conjecture that such clusters provide a low-cost, energy-efficient, flexible, and ultimately effective platform to tackle a wide range of problems within this trilogy. This is a work in progress, and we now elaborate our position and give a preliminary status report. A wide range of Android tablet devices are available in terms of price and capabilities. “You get what you pay for” w.r.t. display resolution, sensors, and chipset---corresponding to the trilogy. $200 gets one a 1280x800-pixel touch display, …

Software Health Management With Bayesian Networks, Johann Schumann, Timmy Mbaya, Ole J. Mengshoel, Knot Pipatsrisawat, Ashok Srivastava, Arthur Choi, Adnan Darwiche

Ole J Mengshoel

Tweet4act: Using Incident-Specific Profiles For Classifying Crisis-Related Messages, Soudip Roy Chowdhury, Muhammad Imran, Muhammad Rizwan Asghar, Sihem Amer-Yahia, Carlos Castillo

Muhammad Imran

We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our …

Chunk-Based Ebmt, Jae Dong Kim, Ralf D. Brown, Jaime G. Carbonell

Jaime G. Carbonell

Corpus driven machine translation approaches such as Phrase-Based Statistical Machine Translation and Example-Based Machine Translation have been successful by using word alignment to find translation fragments for matched source parts in a bilingual training corpus. However, they still cannot properly deal with systematic translation for insertion or deletion words between two distant languages. In this work, we used syntactic chunks as translation units to alleviate this problem, improve alignments and show improvement in BLEU for Korean to English and Chinese to English translation tasks.

Cost Complexity Of Proactive Learning Via A Reduction To Realizable Active Learning, Liu Yang, Jaime G. Carbonell

Jaime G. Carbonell

Proactive Learning is a generalized form of active learning with multiple oracles exhibiting different reliabilities (label noise) and costs. We propose a general approach for Proactive Learning that explicitly addresses the cost vs. reliability tradeoff for oracle and instance selection. We formulate the problem in the PAC learning framework with bounded noise, and transform it into realizable active learning via a reduction technique, while keeping the overall query cost small. We propose two types of sequential hypothesis tests (denoted as SeqHT) that estimate the label of a given query from the noisy replies of different oracles with varying reliabilities and …

Analysis Of Uncertain Data: Evaluation Of Given Hypotheses, Anatole Gershman, Eugene Fink, Bin Fu, Jaime G. Carbonell

Jaime G. Carbonell

We consider the problem of heuristic evaluation of given hypotheses based on limited observations, in situations when available data are insufficient for rigorous statistical analysis.

Active Learning And Crowd-Sourcing For Machine Translation, Vamshi Ambati, Stephan Vogel, Jaime G. Carbonell

Jaime G. Carbonell

In recent years, corpus based approaches to machine translation have become predominant, with Statistical Machine Translation (SMT) being the most actively progressing area. Success of these approaches depends on the availability of parallel corpora. In this paper we propose Active Crowd Translation (ACT), a new paradigm where active learning and crowd-sourcing come together to enable automatic translation for low-resource language pairs. Active learning aims at reducing cost of label acquisition by prioritizing the most informative data for annotation, while crowd-sourcing reduces cost by using the power of the crowds to make do for the lack of expensive language experts. We …

A Probabilistic Framework To Learn From Multiple Annotators With Time-Varying Accuracy, Pinar Donmez, Jaime G. Carbonell, Jeff Schneider

Jaime G. Carbonell

This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based on Sequential Bayesian Estimation to learn the expected accuracy at each time step while simultaneously deciding which annotators to query for a label in an incremental learning framework. We develop a variant of the particle filtering method that estimates the expected accuracy at every time step by sets of weighted samples and performs sequential Bayes updates. The estimated expected accuracies are then used to decide which annotators to be queried at the next time step. …

Alternative Paths In Hiv-1 Targeted Human Signal Transduction Pathways, Sivaraman Balakrishnan, Oznur Tastan, Jaime Carbonell, Judith Klein-Seetharaman

Jaime G. Carbonell

Background:

Human immunodeficiency virus-1 (HIV-1) has a minimal genome of only 9 genes, which encode 15 proteins. HIV-1 thus depends on the human host for virtually every aspect of its life cycle. The universal language of communication in biological systems, including between pathogen and host, is via signal transduction pathways. The fundamental units of these pathways are protein protein interactions. Understanding the functional significance of HIV-1, human interactions requires viewing them in the context of human signal transduction pathways.

Results:

Integration of HIV-1, human interactions with known signal transduction pathways indicates that the majority of known human pathways have the …

Analysis Of Uncertain Data: Smoothing Of Histograms, Eugene Fink, Ankur Sarin, Jaime G. Carbonell

Jaime G. Carbonell

We consider the problem of converting a set of numeric data points into a smoothed approximation ofthe underlying probability distribution. We describe arepresentation of distributions by histograms with variable-width bars, and give a greedy smoothing algorithm based on this representation.

Active Learning For Membrane Protein Structure Prediction, Hatice U. Osmanbeyoglu, Jessica A. Wehner, Jaime G. Carbonell, Madhavi K. Ganapathiraju

Jaime G. Carbonell

Background: About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few …

Temporal Collaborative Filtering With Bayesian Probabilistic Tensor Factorization, Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell

Jaime G. Carbonell

Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account. By introducing additional factors for time, we formalize this problem as a tensor factorization with a special constraint on the time dimension. Further, we provide a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. To learn the model we develop an e±cient sampling procedure that is capable of analyzing large-scale data sets. This new algorithm, called Bayesian Probabilistic Tensor Factorization …

Pairwise Document Classification For Relevance Feedback, Jonathan L. Elsas, Pinar Donmez, Jaime Callan, Jaime G. Carbonell

Jaime G. Carbonell

In this paper we present Carnegie Mellon University's submission to the TREC 2009 Relevance Feedback Track. In this submission we take a classi cation approach on document pairs to using relevance feedback information. We explore using textual and non-textual document-pair features to classify unjudged documents as relevant or non-relevant, and use this prediction to re-rank a baseline document retrieval. These features include co-citation measures, URL similarities, as well as features often used in machine learning systems for document ranking such as the difference in scores assigned by the baseline retrieval system.

Graph-Structured Multi-Task Regression And An Efficient Optimization Method For General Fused Lasso Manuscript, Xi Chen, Seyoung Kim, Qihang Lin, Jaime G. Carbonell, Eric P. Xing

Jaime G. Carbonell

We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2 -regularized multi-task regression assume that all of the output variables are equally related to the inputs, although in many real-world problems, outputs are related in a complex manner. In this paper, we propose graph-guided fused lasso (GFlasso) for structured multi-task regression that exploits the graph structure over the output variables. We introduce a novel penalty …

Understanding Widespread Changes: A Taxonomic Study, Shaowei Wang, David Lo, Lingxiao Jiang

David LO

Many active research studies in software engineering, such as detection of recurring bug fixes, detection of copyand- paste bugs, and automated program transformation tools, are motivated by the assumption that many code changes (e.g., changing an identifier name) in software systems are widespread to many locations and are similar to one another. However, there is no study so far that actually analyzes widespread changes in software systems. Understanding the nature of widespread changes could empirically support the assumption, which provides insight to improve the research studies and related tools. Our study in this paper addresses such a need. We propose …

Diffusion Of Software Features: An Exploratory Study, Ferdian Thung, David Lo, Lingxiao Jiang

David LO

New features are frequently proposed in many software libraries. These features include new methods, classes, packages, etc. These features are utilized in many open source and commercial software systems. Some of these features are adopted very quickly, while others take a long time to be adopted. Each feature takes much resource to develop, test, and document. Library developers and managers need to decide what feature to prioritize and what to develop next. As a first step to aid these stakeholders, we perform an exploratory study on the diffusion or rate of adoption of features in Java Development Kit (JDK) library. …

Predicting Project Outcome Leveraging Socio-Technical Network Patterns, Didi Surian, Yuan Tian, David Lo, Hong Cheng, Ee Peng Lim

David LO

There are many software projects started daily, some are successful, while others are not. Successful projects get completed, are used by many people, and bring benefits to users. Failed projects do not bring similar benefits. In this work, we are interested in developing an effective machine learning solution that predicts project outcome (i.e., success or failures) from developer socio-technical network. To do so, we investigate successful and failed projects to find factors that differentiate the two. We analyze the socio-technical aspect of the software development process by focusing at the people that contribute to these projects and the interactions among …

Network Structure Of Social Coding In Github, Ferdian Thung, Tegawende F. Bissyande, David Lo, Lingxiao Jiang

David LO

Social coding enables a different experience of software development as the activities and interests of one developer are easily advertized to other developers. Developers can thus track the activities relevant to various projects in one umbrella site. Such a major change in collaborative software development makes an investigation of networkings on social coding sites valuable. Furthermore, project hosting platforms promoting this development paradigm have been thriving, among which GitHub has arguably gained the most momentum. In this paper, we contribute to the body of knowledge on social coding by investigating the network structure of social coding in GitHub. We collect …

Adoption Of Software Testing In Open Source Projects: A Preliminary Study On 50,000 Projects, Pavneet Singh Kochhar, Tegawende F. Bissyande, David Lo, Lingxiao Jiang

David LO

In software engineering, testing is a crucial activity that is designed to ensure the quality of program code. For this activity, development teams spend substantial resources constructing test cases to thoroughly assess the correctness of software functionality. What is however the proportion of open source projects that include test cases? What kind of projects are more likely to include test cases? In this study, we explore 50,000 projects and investigate the correlation between the presence of test cases and various project development characteristics, including the lines of code and the size of development teams.

Immaccs: A Multi-Agent Decision-Support System, Jens G. Pohl, Mark Porczak, Kym Jason Pohl, Russell Leighton, Hisham Assal, Alan Davis, Lakshmi Vempati, Anthony Wood

Hisham Assal

This report describes work performed by the Collaborative Agent Design Research Center for the US Marine Corps Warfighting Laboratory (MCWL), on the IMMACCS experimental decision-support system. IMMACCS (Integrated Marine Multi-Agent Command and Control System) incorporates three fundamental concepts that distinguish it from existing (i.e., legacy) command and control applications. First, it is a collaborative system in which computer-based agents assist human operators by monitoring, analyzing, and reasoning about events in near real-time. Second, IMMACCS includes an ontological model of the battlespace that represents the behavioral characteristics and relationships among real world entities such as friendly and enemy assets, infrastructure objects …

Using Bpm As An Interoperability Platform, Dennis Taylor, Hisham Assal, Jens G. Pohl

Hisham Assal

Data mediation is an essential component in the Modeling and Simulation field (M&S). Managing multiple data sources and exchanging data among multiple systems requires sophisticated tools and a powerful process management system. Business Process Management (BPM) provides a framework for modeling and managing business activities, both manual and automated, in a consistent manner. Managing automated processes offers an opportunity to integrate external applications into the platform. By integrating automated data transformation tools into the business processes using graphical programming, we provide an approach to achieve operational interoperability among diverse applications without the need for any application to be aware of …

The Representation Of Context In Computer Software, Hisham Assal, Kym Pohl, Jens G. Pohl

Hisham Assal

Computers do not have the equivalent of a human cognitive system and therefore store data simply as the numbers and words that are entered into the computer. For a computer to interpret data it requires an information structure that provides at least some level of context. This can be accomplished utilizing an ontology of objects with characteristics, semantic behavior, and a rich set of relationships to create a virtual version of real world situations and provide the context within which intelligent logic (e.g., agents) can automatically operate. This paper discusses the process of developing ontologies that serve to provide context …