Numerical Analysis and Scientific Computing Commons^™

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

Comparing Tensorflow Deep Learning Performance Using Cpus, Gpus, Local Pcs And Cloud, John Lawrence, Jonas Malmsten, Andrey Rybka, Daniel A. Sabol, Ken Triplin

Publications and Research

Deep learning is a very computational intensive task. Traditionally GPUs have been used to speed-up computations by several orders of magnitude. TensorFlow is a deep learning framework designed to improve performance further by running on multiple nodes in a distributed system. While TensorFlow has only been available for a little over a year, it has quickly become the most popular open source machine learning project on GitHub. The open source version of TensorFlow was originally only capable of running on a single node while Google’s proprietary version only was capable of leveraging distributed systems. This has now changed. In this …

Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly …

Predicting The Impact Of Software Engineering Topics: An Empirical Study, Santonu Sarkar, Rumana Lakdawala, Subhajit Datta

Research Collection School Of Computing and Information Systems

Predicting the future is hard, more so in active research areas. In this paper, we customize an established model for citation prediction of research papers and apply it on research topics. We argue that research topics, rather than individual publications, have wider relevance in the research ecosystem, for individuals as well as organizations. In this study, topics are extracted from a corpus of software engineering publications covering 55,000+ papers written by more than 70,000 authors across 56 publication venues, over a span of 38 years, using natural language processing techniques. We demonstrate how critical aspects of the original paper-based prediction …

Characterizing Malicious Android Apps By Mining Topic-Specific Data Flow Signatures, Xinli Yang, David Lo, Li Li, Xin Xia, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

Context: State-of-the-art works on automated detection of Android malware have leveraged app descriptions to spot anomalies w.r.t the functionality implemented, or have used data flow information as a feature to discriminate malicious from benign apps. Although these works have yielded promising performance,we hypothesize that these performances can be improved by a better understanding of malicious behavior. Objective: To characterize malicious apps, we take into account both information on app descriptions,which are indicative of apps’ topics, and information on sensitive data flow, which can be relevant todiscriminate malware from benign apps. Method: In this paper, we propose a topic-specific approach to …

K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler

Graduate Student Theses, Dissertations, & Professional Papers

Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …

Data Mining By Grid Computing In The Search For Extrasolar Planets, Oisin Creaner [Thesis]

Doctoral

A system is presented here to provide improved precision in ensemble differential photometry. This is achieved by using the power of grid computing to analyse astronomical catalogues. This produces new catalogues of optimised pointings for each star, which maximise the number and quality of reference stars available. Astronomical phenomena such as exoplanet transits and small-scale structure within quasars may be observed by means of millimagnitude photometric variability on the timescale of minutes to hours. Because of atmospheric distortion, ground-based observations of these phenomena require the use of differential photometry whereby the target is compared with one or more reference stars. …

Software Engineering For Science, Jeffrey C. Carver, Neil P. Chue Hong, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced, and which software engineering practices are effective for scientific software.

The book starts with a detailed overview of the Scientific Software Lifecycle, and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development, as well as solutions to these problems.

The second part of the book provides examples of the use of …

Computer-Aided Design Of Algorithms Of Pulsed Control Of Arc Welding Process Based On Numerical Simulation, Oksana I. Shpigunova, Anatoliy A. Glazunov

The 8th International Conference on Physical and Numerical Simulation of Materials Processing

No abstract provided.

How Long Will This Live? Discovering The Lifespans Of Software Engineering Ideas, Subhajit Datta, Santonu Sarkar, A. S. M Sajeev

Research Collection School Of Computing and Information Systems

We all want to be associated with long lasting ideas; as originators, or at least, expositors. For a tyro researcher or a seasoned veteran, knowing how long an idea will remain interesting in the community is critical in choosing and pursuing research threads. In the physical sciences, the notion of half-life is often evoked to quantify decaying intensity. In this paper, we study a corpus of 19,000+ papers written by 21,000+ authors across 16 software engineering publication venues from 1975 to 2010, to empirically determine the half-life of software engineering research topics. In the absence of any consistent and well-accepted …

Ontology-Aided Feature Correlation For Multi-Modal Urban Sensing, Archan Misra, Zaman Lantra, Kasthuri Jayarajah

Research Collection School Of Computing and Information Systems

The paper explores the use of correlation across features extracted from different sensing channels to help in urban situational understanding. We use real-world datasets to show how such correlation can improve the accuracy of detection of city-wide events by combining metadata analysis with image analysis of Instagram content. We demonstrate this through a case study on the Singapore Haze. We show that simple ontological relationships and reasoning can significantly help in automating such correlation-based understanding of transient urban events.

An Optical Character Recognition Engine For Graphical Processing Units, Jeremy Reed

Theses and Dissertations--Computer Science

This dissertation investigates how to build an optical character recognition engine (OCR) for a graphical processing unit (GPU). I introduce basic concepts for both building an OCR engine and for programming on the GPU. I then describe the SegRec algorithm in detail and discuss my findings.

Demo: Smartwatch Based Food Diary And Eating Analytics, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Monitoring an individual’s daily dietary intake can provide various insights regarding the health of the individual. Applications such as My Fitness Pal exists, which allows individuals to monitor all items that the individual consumed. However, manual monitoring can be labour intensive. To overcome this limitation, wrist worn sensor based eating habit monitoring has been studied by various researchers. These systems can detect eating gesture, but they cannot determine what is being eaten. We have built a system which can (i) detect eating gesture using the smartwatch's inertial sensors (ii) use the smartwatch's camera to capture images of food consumed at …

Energy Forecasting For Event Venues: Big Data And Prediction Accuracy, Katarina Grolinger, Alexandra L'Heureux, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy …

An Immersive Telepresence System Using Rgb-D Sensors And Head-Mounted Display, Xinzhong Lu, Ju Shen, Saverio Perugini, Jianjun Yang

Computer Science Faculty Publications

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to effectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The …

Choosing Your Weapons: On Sentiment Analysis Tools For Software Engineering Research, Robbert Jongeling, Subhajit Datta, Alexander Serebrenik

Research Collection School Of Computing and Information Systems

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact …

The Importance Of Being Isolated: An Empirical Study On Chromium Reviews, Subhajit Datta, Devarshi Bhatt, Manish Jain, Proshanta Sarkar, Santonu Sarkar

Research Collection School Of Computing and Information Systems

As large scale software development has become more collaborative, and software teams more globally distributed, several studies have explored how developer interaction influences software development outcomes. The emphasis so far has been largely on outcomes like defect count, the time to close modification requests etc. In the paper, we examine data from the Chromium project to understand how different aspects of developer discussion relate to the closure time of reviews. On the basis of analyzing reviews discussed by 2000+ developers, our results indicate that quicker closure of reviews owned by a developer relates to higher reception of information and insights …

Need Accurate User Behaviour?: Pay Attention To Groups!, Kasthuri Jayarajah, Youngki Lee, Archan Misra, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we show that characterizing user behaviour from location or smartphone usage traces, without accounting for the interaction of individuals in physical-world groups, can lead to erroneous results. We conducted one of the largest studies in the UbiComp domain thus far, involving indoor location traces of more than 6,000 users, collected over a 4-month period at our university campus, and further studied fine-grained App usage of a subset of 156 Android users. We apply a state-of-the-art group detection algorithm to annotate such location traces with group vs. individual context, and then show that individuals vs. groups exhibit significant …

Deep Learning For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun

Research Collection School Of Computing and Information Systems

Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of …

Enabling Real Time In-Situ Context Based Experimentation To Observe User Behaviour, Kartik Muralidaran

Dissertations and Theses Collection (Open Access)

Today’s mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. These devices are also part of the everyday life of millions of people, and coupled with the unprecedented access to personal context, make them the ideal tool for conducting behavioural experiments in an unobtrusive way. Transforming the mobile device from a mere observer of human context to an enabler of behavioural experiments however, requires not only providing experimenters access to the deep, near-real time human context (e.g., location, activity, group dynamics) but also exposing a disciplined scientific experimentation service that frees them from …

Cooperation In Delay-Tolerant Networks With Wireless Energy Transfer: Performance Analysis And Optimization, Dusit Niyato, Ping Wang, Hwee-Pink Tan, Walid Saad, Dong In Kim

Research Collection School Of Computing and Information Systems

We consider a delay-tolerant network (DTN) whose mobile nodes are assigned to collect packets from data sources and deliver them to a sink (i.e., a gateway). Each mobile node operates by using energy transferred wirelessly from the gateway. For such a network, two main issues are studied. First, when a mobile node is at the data source, this node must decide on whether to accept the packet received from the data source or not. In contrast, whenever a mobile node is at the gateway, it has to decide on whether to transmit the packets collected from the data sources or …

Automatic Video Self Modeling For Voice Disorder, Ju Shen, Changpeng Ti, Anusha Raghunathan, Sen-Ching S. Cheung, Rita Patel

Computer Science Faculty Publications

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with fluency disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice disorders. Starting with a video recording of a voice-disorder patient, the proposed …

Compression Of Video Tracking And Bandwidth Balancing Routing In Wireless Multimedia Sensor Networks, Yin Wang, Jianjun Yang, Ju Shen, Bryson Payne, Juan Guo, Kun Hua

Computer Science Faculty Publications

There has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSNs) have become the premier choice in many research communities and industry. Many state-of-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSNs. The transmission speed is constrained by the big file size of video data and fixed bandwidth allocation in constant routing paths. In this paper, we present a CamShift based algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which each sensor node is able to dynamically select …

Leading Undergraduate Students To Big Data Generation, Jianjun Yang, Ju Shen

Computer Science Faculty Publications

People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students hands-on …

Hole Detection And Shape-Free Representation And Double Landmarks Based Geographic Routing In Wireless Sensor Networks, Jianjun Yang, Zongming Fei, Ju Shen

Computer Science Faculty Publications

In wireless sensor networks, an important issue of geographic routing is “local minimum” problem, which is caused by a “hole” that blocks the greedy forwarding process. Existing geographic routing algorithms use perimeter routing strategies to find a long detour path when such a situation occurs. To avoid the long detour path, recent research focuses on detecting the hole in advance, then the nodes located on the boundary of the hole advertise the hole information to the nodes near the hole. Hence the long detour path can be avoided in future routing. We propose a heuristic hole detecting algorithm which identifies …

Will This Be Quick? A Case Study Of Bug Resolution Times Across Industrial Projects, Subhajit Datta, Prasanth Lade

Research Collection School Of Computing and Information Systems

Resolution of problem tickets is a source of significant revenue in the worldwide software services industry. Due to the high volume of problem tickets in any large scale customer engagement, automated techniques are necessary to segregate related incoming tickets into groups. Existing techniques focus on this classification problem. In this paper, we present a case study built around the position that predicting the category of resolution times within a class of tickets and also the actual resolution times, is strongly beneficial to ticket resolution. We present an approach based on topic analysis to predict the category of resolution times of …

Mapping Legume Roots Can Determine Best Performing Crops, Frédérique C. Guinel

Clear Language Summaries

Legume plants have a mutually beneficial relationship with bacteria. These beneficial bacteria live in nodules found on plant roots. Until recently there was no way to systematically measure the roots the nodules are found on and to place accurately the nodules on these roots. The research team created software and a method to measure multiple root parameters which were not available previously. This allows different plant varieties to be compared with precise measurements of the root structure in order to figure out which plants will grow better.

Energy Cost Forecasting For Event Venues, Katarina Grolinger, Andrea Zagar, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning …

Structure Preserving Large Imagery Reconstruction, Ju Shen, Jianjun Yang, Sami Taha Abu Sneineh, Bryson Payne, Markus Hitz

Computer Science Faculty Publications

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Furthermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image …

How Many Eyeballs Does A Bug Need? An Empirical Validation Of Linus' Law, Subhajit Datta, Proshanta Sarkar, Sutirtha Das, Sonu Sreshtha, Prasanth Lade, Subhashis Majumder

Research Collection School Of Computing and Information Systems

Linus’ Law reflects on a key characteristic of open source software development: developers’ tendency to closely work together in the bug resolution process. In this paper we empirically examine Linus’ Law using a data-set of 1,000+ Android bugs, owned by 70+ developers. Our results indicate that encouraging developers to work closely with one another has nuanced implications; while one form of contact may help reduce bug resolution time, another form can have quite the opposite effect. We present statistically significant evidence in support of our results and discuss their relevance at the individual and organizational levels.