Software Engineering Commons^™

Computational Thinking And Literacy, Sharin Rawhiya Jacob, Mark Warschauer

Journal of Computer Science Integration

Today’s students will enter a workforce that is powerfully shaped by computing. To be successful in a changing economy, students must learn to think algorithmically and computationally, to solve problems with varying levels of abstraction. These computational thinking skills have become so integrated into social function as to represent fundamental literacies. However, computer science has not been widely taught in K-12 schools. Efforts to create computer science standards and frameworks have yet to make their way into mandated course requirements. Despite a plethora of research on digital literacies, research on the role of computational thinking in the literature is sparse. …

Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa

Boise State University Theses and Dissertations

Describing scenes such as rooms, city streets, or routes, is a very common human task that requires the ability to identify and describe the scene sufficiently for a hearer to develop a mental model of the scene. When people talk about such scenes, they mention some objects of the scene at the exclusion of others. We call the mentioned objects salient objects as people consider them noticeable or important in comparison to other non-mentioned objects. In this thesis, we look at saliency of visual scenes and how visual saliency informs what can and should be said about a scene when …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

The Effect Of Endgame Tablebases On Modern Chess Engines, Christopher D. Peterson

Computer Engineering

Modern chess engines have the ability to augment their evaluation by using massive tables containing billions of positions and their memorized solutions. This report examines the importance of these tables to better understand the circumstances under which they should be used. The analysis conducted in this paper empirically examines differences in size and speed of memorized positions and their impacts on engine strength. Using this technique, situations where memorized tables improve play (and situations where they do not) are discovered.

Analysing Multi-Point Multi-Frequency Machine Vibrations Using Optical Sampling, Dibyendu Roy, Avik Ghose, Tapas Chakravarty, Sushovan Mukherjee, Arpan Pal, Archan Misra

Research Collection School Of Computing and Information Systems

Vibration analysis is a key troubleshooting methodology for assessing the health of factory machinery. We propose an unobtrusive framework for at-a-distance visual estimation of such (possibly high frequency) vibrations, using a low fps (frames-per-second) camera that may, for example, be mounted on a worker's smart-glass. Our key innovation is to use an external stroboscopic light source (that, for example, may be provided by an assistive robot), to illuminate the machine with multiple mutually-prime strobing frequencies, and use the resulting aliased signals to efficiently estimate the different vibration frequencies via an enhanced version of the Chinese Remainder Theorem. Experimental results show …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Energy Slices: Benchmarking With Time Slicing, Katarina Grolinger, Hany F. Elyamany, Wilson Higashino, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants’ wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. …

Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr

Journal of International Technology and Information Management

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain …

Capsense: Capacitor-Based Activity Sensing For Kinetic Energy Harvesting Powered Wearable Devices, Guohao Lan, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion …

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 …

Adviser+: Toward A Usable Web-Based Algorithm Portfolio Deviser, Hoong Chuin Lau, Mustafa Misir, Xiang Li Li, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

The present study offers a more user-friendly and parallelized version of a web-based algorithm portfolio generator, called ADVISER. ADVISER is a portfolio generation tool to deliver a group of configurations for a given set of algorithms targeting a particular problem. The resulting configurations are expected to be diverse such that each can perform well on a certain type of problem instances. One issue with ADVISER is that it performs portfolio generation on a single-core which results in long waiting times for the users. Besides that, it lacks of a reporting system with visualizations to tell more about the generated portfolios. …

On The Similarities Between Random Regret Minimization And Mother Logit: The Case Of Recursive Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger

Research Collection School Of Computing and Information Systems

This paper focuses on the comparison of the random regret minimization (RRM) and mother logit models for analyzing the choice between alternatives having deterministic attributes. The mother logit model allows utilities of a given alternative to depend on attributes of other alternatives. It was designed to relax the independence from irrelevant alternatives (IIA) property while keeping the random terms independently and identically distributed extreme value distributed (McFadden et al., 1978).We adapt and extend the RRM model proposed by Chorus (2014) to the case of recursive logit (RL) route choice models (Fosgerau et al., 2013). We argue that these RRM models …

An Open Source Discussion Group Recommendation System, Sarika Padmashali

Master's Projects

A recommendation system analyzes user behavior on a website to make suggestions about what a user should do in the future on the website. It basically tries to predict the “rating” or “preference” a user would have for an action. Yioop is an open source search engine, wiki system, and user discussion group system managed by Dr. Christopher Pollett at SJSU. In this project, we have developed a recommendation system for Yioop where users are given suggestions about the threads and groups they could join based on their user history. We have used collaborative filtering techniques to make recommendations and …

A Chatbot Framework For Yioop, Harika Nukala

Master's Projects

Over the past few years, messaging applications have become more popular than Social networking sites. Instead of using a specific application or website to access some service, chatbots are created on messaging platforms to allow users to interact with companies’ products and also give assistance as needed. In this project, we designed and implemented a chatbot Framework for Yioop. The goal of the Chatbot Framework for Yioop project is to provide a platform for developers in Yioop to build and deploy chatbot applications. A chatbot is a web service that can converse with users using artificial intelligence in messaging platforms. …

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 …

Music Feature Matching Using Computer Vision Algorithms, Mason Hollis

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as …

Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan

Research Collection School Of Computing and Information Systems

Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the following criteria: high resource utilization, fair resource allocation and low sharing overhead. To solve this problem, we propose a new CMS named Dorm, incorporating a dynamicallypartitioned cluster management mechanism and an utilizationfairness optimizer. Specifically, Dorm uses the container-based virtualization technique to partition a cluster, runs one application per partition, and can dynamically resize each partition at application runtime for resource efficiency and fairness. Each application directly launches …

Real-Time Vision-Based Lane Detection With 1d Haar Wavelet Transform On Raspberry Pi, Vikas Reddy Sudini

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Rapid progress is being made towards the realization of autonomous cars. Since the technology is in its early stages, human intervention is still necessary in order to ensure hazard-free operation of autonomous driving systems. Substantial research efforts are underway to enhance driver and passenger safety in autonomous cars. Toward that end GreedyHaarSpiker, a real-time vision-based lane detection algorithm is proposed for road lane detection in different weather conditions. The algorithm has been implemented in Python 2.7 with OpenCV 3.0 and tested on a Raspberry Pi 3 Model B ARMv8 1GB RAM coupled to a Raspberry Pi camera board v2. To …

Follow-My-Lead: Intuitive Indoor Path Creation And Navigation Using See-Through Interactive Videos, Quentin Roy, Simon T. Perrault, Shengdong Zhao, Richard Davis, Anuroop Pattena Vaniyar, Velko Vechev, Youngki Lee, Archan Misra

Research Collection School Of Computing and Information Systems

We present Follow-My-Lead, an alternative indoor navigation technique that uses visual information recorded on an actual navigation path as a navigational guide. Its design revealed a trade-off between the fidelity of information provided to users and their effort to acquire it. Our first experiment revealed that scrolling through a continuous image stream of the navigation path is highly informative, but it becomes tedious with constant use. Discrete image checkpoints require less effort, but can be confusing. A balance may be struck by adding fast video transitions between image checkpoints, but precise control is required to handle difficult situations. Authoring still …

Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide

Research Collection School Of Computing and Information Systems

Singapore's "smart city" agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined …

Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning …

Smart Homes Enhance Seniors’ Safety, Singapore Management University

Research@SMU: Connecting the Dots

Professor Tan Hwee Pink and researchers at iCity Lab are using sensors to increase the safety of seniors who live independently in their own homes.

See the papers:

• Online detection of behavioral change using unobtrusive eldercare monitoring system
• Improving the sensitivity of unobtrusive inactivity detection in sensor-enabled homes for the elderly
• SHINESeniors: Personalized services for active ageing-in-place

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …

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 …

Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …

A Decomposition Method For Estimating Recursive Logit Based Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger

Research Collection School Of Computing and Information Systems

Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We design a decomposition (DeC) method in order to reduce the number of linear systems to be solved, opening the possibility to estimate more complex RL based models, for instance mixed RL models. We test the performance of the DeC method by estimating the RL model on two networks …

Bilevel Model-Based Discriminative Dictionary Learning For Recognition, Pan Zhou, Chao Zhang, Lin Zhouchen

Research Collection School Of Computing and Information Systems

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the 0 or 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity …

Collecting Image Cropping Dataset: A Hybrid System Of Machine And Human Intelligence, Uyen T. Mai, Feng Liu

Student Research Symposium

Image cropping is a common tool that exists in almost any image editor, yet automatic cropping is still a difficult problem in Computer Vision. Since images nowadays can be easily collected through the web, machine learning is a promising approach to solve this problem. However, an image cropping dataset is not yet available and gathering such a large-scale dataset is a non-trivial task. Although a crowdsourcing website such as Mechanical Turk seems to be a solution to this task, image cropping is a sophisticated task that is vulnerable to unreliable annotation; furthermore, collecting a large-scale high-quality dataset through crowdsourcing is …

Ant Colony Optimization For Continuous Spaces, Rachel Findley

Computer Science and Computer Engineering Undergraduate Honors Theses

Ant Colony Optimization (ACO) is an optimization algorithm designed to find semi-optimal solutions to Combinatorial Optimization Problems. The challenge of modifying this algorithm to effectively optimize over a continuous domain is one that has been tackled by several researchers. In this paper, ACO has been modified to use several variations of the algorithm for continuous spaces. An aspect of ACO which is crucial to its success when optimizing over a continuous space is choosing the appropriate object (solution component) out of an infinite set to add to the ant's path. This step is highly important in shaping good solutions. Important …