Computer Sciences Commons^™

Ontology Based Technical Skill Similarity, Yeshwanth Bashyam Balachander

Master's Projects

Online job boards have become a major platform for technical talent procurement and job search. These job portals have given rise to challenging matching and search problems. The core matching or search happens between technical skills of the job requirements and the candidate's profile or keywords. The extensive list of technical skills and its polyonymous nature makes it less effective to perform a direct keyword matching. This results in substandard job matching or search results which misses out a closely matching candidate on account of it not having the exact skills. It is important to use a semantic similarity measure …

Feature Selection Using Genetic Algorithms, Vandana Kannan

Master's Projects

With the large amount of data of different types that are available today, the number of features that can be extracted from it is huge. The ever-increasing popularity of multimedia applications, has been a major factor for this, especially in the case of image data. Image data is used for several applications such as classification, retrieval, object recognition, and annotation. Often, utilizing the entire feature set for each of these activities can be not only be time consuming but can also negatively impact the performance. Given the large number of features, it is difficult to find the subset of features …

Boosted Hidden Markov Models For Malware Detection, Aditya Raghavan

Master's Projects

Digital security is an important issue today, and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has found widespread application in the field of pattern matching and malware detection is hidden Markov models (HMMs). Since HMM training is a hill climb technique, we can often significantly improve a model by training multiple times with different initial values. In this research, we compare boosted HMMs (using AdaBoost) to HMMs trained …

Using Sentiment Analysis And Pattern Matching To Signal User Review Abnormalities, Stefan S. Gloutnikov

Master's Projects

User opinions on websites like Amazon, Yelp, and TripAdvisor are a key input for consumers when figuring out what to purchase, or where and what to eat. This means that in order for such websites to provide a better service to their customers, they must guard against fake and targeted reviews. Detecting such users and reviews automatically is a very complex multi-step process, and there is no direct mechanism for solving the problem reliably. Multiple AI and Machine Learning algorithms are coupled together when examining user reviews in determining if a review is fake or not. In this project we …

Anomaly Detection For Application Log Data, Aarish Grover

Master's Projects

In software development, there is an absolute requirement to ensure that a system once developed, functions at its best throughout its lifetime. Application log data is critical to maintaining application performance and thus techniques to parse, understand and detect anomalies in application log data are critical to ensuring efficiency in software development. While initially hampered by limited hardware and lack of quality datasets, anomaly detection techniques have recently received a surge of interest with advancements in machine learning technology and especially neural networks. In this paper, we explore anomaly detection, historical techniques to detect anomalies and recent advancements in neural …

Visual Question Answering, Pankti Kansara

Master's Projects

There has been immense progress in the fields of computer vision, object detection and natural language processing (NLP) in recent years. Artificial Intelligence (AI) systems, such as question answering models, use NLP to provide a "comprehensive" capability to the machine. Such a machine can answer natural language queries about any portion of an unstructured text. An extension of this system is to combine NLP with computer vision to accomplish the task of Visual Question Answering (VQA), which is to build a system that can answer natural language queries about images. A number of systems have been proposed for VQA that …

Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo

Master's Projects

There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an …

Cannonical Error Analysis In Introductory Programming - Call For Participation, William L. Honig

Computer Science: Faculty Publications and Other Works

Cal for interested researchers and faculty to refine a set of canonical error categories to be used in analysis of student programming projects during initial programming courses (CS1, CS2). Interest? Contact Dr. William HONIG, Loyola University Chicago, [email protected]

Frame Inference For Inductive Entailment Proofs In Separation Logic, Quang Loc Le, Jun Sun, Shengchao Qin

Research Collection School Of Computing and Information Systems

Given separation logic formulae A and C, frame inference is the problem of checking whether A entails C and simultaneously inferring residual heaps. Existing approaches on frame inference do not support inductive proofs with general inductive predicates. In this work, we present an automatic frame inference approach for an expressive fragment of separation logic. We further show how to strengthen the inferred frame through predicate normalization and arithmetic inference. We have integrated our approach into an existing verification system. The experimental results show that our approach helps to establish a number of non-trivial inductive proofs which are beyond the capability …

Tracing Actin Filament Bundles In Three-Dimensional Electron Tomography Density Maps Of Hair Cell Stereocilia, Salim Sazzed, Junha Song, Julio Kovacs, Willi Wriggers, Manfred Auer, Jing He

Computer Science Faculty Publications

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin …

Prediction Of Lncrna-Disease Associations Based On Inductive Matrix Completion, Chengqian Lu, Mengyun Yang, Feng Luo, Fang-Xiang Wu, Min Li, Yi Pan, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA–disease associations, especially for novel lncRNAs, remains challenging.

Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA– disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA–disease interactions and functional similarity of diseases based on disease–gene and gene–gene onotology …

Entagrec(++): An Enhanced Tag Recommendation System For Software Information Sites, Shaowei Wang, David Lo, Bogdan Vasilescu, Alexander Serebrenik

Research Collection School Of Computing and Information Systems

Software engineers share experiences with modern technologies by means of software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. However, tags assigned to objects tend to be noisy and some objects are not well tagged. To improve the quality of tags in software information sites, we propose EnTagRec, an automatic tag recommender based on historical tag assignments to software objects and we evaluate its performance on four software information sites, Stack Overflow, Ask Ubuntu, Ask Different, and Free code. We observe that that …

Supporting Big Data At The Vehicular Edge, Lloyd Decker

Computer Science Theses & Dissertations

Vehicular networks are commonplace, and many applications have been developed to utilize their sensor and computing resources. This is a great utilization of these resources as long as they are mobile. The question to ask is whether these resources could be put to use when the vehicle is not mobile. If the vehicle is parked, the resources are simply dormant and waiting for use. If the vehicle has a connection to a larger computing infrastructure, then it can put its resources towards that infrastructure. With enough vehicles interconnected, there exists a computing environment that could handle many cloud-based application services. …

Octopus: An Online Topic-Aware Influence Analysis System For Social Networks, Ju Fan, Jiarong Qiu, Yuchen Li, Qingfei Meng, Dongxiang Zhang, Guoliang Li, Kian-Lee Tan, Xiaoyong Du

Research Collection School Of Computing and Information Systems

The wide adoption of social networks has brought a new demand on influence analysis. This paper presents OCTOPUS that offers social network users and analysts valuable insights through topic-aware social influence analysis services. OCTOPUS has the following novel features. First, OCTOPUS provides a user-friendly interface that allows users to employ simple and easy-to-use keywords to perform influence analysis. Second, OCTOPUS provides three powerful keyword-based topic-aware influence analysis tools: keyword-based influential user discovery, personalized influential keywords suggestion, and interactive influential paths exploration. These tools can not only discover influential users, but also provide insights on how the users influence the network. …

Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely …

A Feasibility Study On Crowdsourcing To Monitor Municipal Resources In Smart Cities, Thivya Kandappu, Archan Misra, Ming Hui, Desmond (Xu Minghui) Koh, Randy Tandriansyah Daratan, Nikita Jaiman

Research Collection School Of Computing and Information Systems

Active citizenry, whereby citizens actively participate inreporting and addressing challenges in urban service delivery is a strategic goalof smart cities such as Singapore. In spite of the promise, we believe that thesuccess of such large-scale nation-wide crowdsourcing deployments depend on thereal-word user preferences and behavioral characteristics of citizens. In thispaper, we first present our findings on behavioral preferences and key concernsof citizens regarding smart-city services via an opinion survey conducted with 1300participants. We then propose a “citizen-controlled” urban services reportingplatform where citizens actively report on the status of various municipalresources. We advocate the importance of matching user mobility patternsagainst task …

Combined Classifier For Cross-Project Defect Prediction: An Extended Empirical Study, Yun Zhang, David Lo, Xin Xia, Jianling Sun

Research Collection School Of Computing and Information Systems

To facilitate developers in effective allocation of their testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on the past history of classes, methods, or certain other code elements. These techniques are effective provided that a sufficient amount of data is available to train a prediction model. However, sufficient training data are rarely available for new software projects. To resolve this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, …

Combined Classifier For Cross-Project Defect Prediction: An Extended Empirical Study, Yun Zhang, David Lo, Xin Xia, Jianling Sun

Research Collection School Of Computing and Information Systems

To help developers better allocate testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of buggy classes. These techniques work well as long as a sufficient amount of data is available to train a prediction model. However, there is rarely enough training data for new software projects. To deal with this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, has been proposed and is regarded as …

Combination Forecasting Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Shunchang Yu, Bin Li, Steven C. H. Hoi, Shuigeng G. Zhou

Research Collection School Of Computing and Information Systems

Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, …

Continuous Top-K Monitoring On Document Streams (Extended Abstract), Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Research Collection School Of Computing and Information Systems

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The objective is to support large numbers of users and high stream rates, while refreshing the topk results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach, …

A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, …

Virtualization In Wireless Sensor Networks: Fault Tolerant Embedding For Internet Of Things, Omprakash Kaiwartya, Abdul Hanan Abdullah, Yue Cao, Jaime Lloret, Sushil Kumar, Rajiv Ratn Shah, Mukesh Prasad, Shiv Prakash

Research Collection School Of Computing and Information Systems

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for IoT. Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering fault tolerance and communication delay as two conflicting objectives. An adapted non-dominated sorting based genetic algorithm (A-NSGA) is developed to …

Pccf: Periodic And Continual Temporal Co-Factorization For Recommender Systems, Guibing Guo, Feida Zhu, Shilin Qu, Xingwei Wang

Research Collection School Of Computing and Information Systems

Rating-only collaborative filtering has been extensively studied for decades with great improvements achieved in predicting a user’s preference on a target item at a particular time point. Yet, it remains a research challenge on how to capture users’ rating patterns which may drift over time. In this article, we propose a time-aware matrix co-factorization model, called PCCF, which considers two types of temporal effects, i.e., periodic and continual. Specifically, periodic effects refer to the impact of discrete periodic time slices with which users’ preferences may be associated, and continual effects refer to the impact of continuous gradual time over which …

Fast And Robust Segmentation Of White Blood Cell Images By Self-Supervised Learning, Xin Zheng, Yong Wang, Guoyou Wang, Jianguo Liu

Research Collection School Of Computing and Information Systems

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module …

A Visual Interaction Cue Framework From Video Game Environments For Augmented Reality, Kody R. Dillman, Terrance Tin Hoi Mok, Anthony Tang, Lora Oehlberg, Alex Mitchell

Research Collection School Of Computing and Information Systems

Based on an analysis of 49 popular contemporary video games, we develop a descriptive framework of visual interaction cues in video games. These cues are used to inform players what can be interacted with, where to look, and where to go within the game world. These cues vary along three dimensions: the purpose of the cue, the visual design of the cue, and the circumstances under which the cue is shown. We demonstrate that this framework can also be used to describe interaction cues for augmented reality applications. Beyond this, we show how the framework can be used to generatively …

Geocaching With A Beam: Shared Outdoor Activities Through A Telepresence Robot With 360 Degree Viewing, Yasamin Heshmat, Brennan Jones, Xiaoxuan Xiong, Carman Neustaedter, Anthony Tang, Bernhard E. Riecke, Lillian Yang

Research Collection School Of Computing and Information Systems

People often enjoy sharing outdoor activities together such as walking and hiking. However, when family and friends are separated by distance it can be difficult if not impossible to share such activities. We explore this design space by investigating the benefits and challenges of using a telepresence robot to support outdoor leisure activities. In our study, participants participated in the outdoor activity of geocaching where one person geocached with the help of a remote partner via a telepresence robot. We compared a wide field of view (WFOV) camera to a 360° camera. Results show the benefits of having a physical …

Perspective On And Re-Orientation Of Physical Proxies In Object-Focused Remote Collaboration, Martin Feick, Terrance Mok, Anthony Tang, Lora Oehlberg, Ehud Sharlin

Research Collection School Of Computing and Information Systems

Remote collaborators working together on physical objects have difficulty building a shared understanding of what each person is talking about. Conventional video chat systems are insufficient for many situations because they present a single view of the object in a flattened image. To understand how this limited perspective affects collaboration, we designed the Remote Manipulator (ReMa), which can reproduce orientation manipulations on a proxy object at a remote site. We conducted two studies with ReMa, with two main findings. First, a shared perspective is more effective and preferred compared to the opposing perspective offered by conventional video chat systems. Second, …

Pageflip: Leveraging Page-Flipping Gestures For Efficient Command And Value Selection On Smartwatches, Teng Han, Jiannan Li, Khalad Hasan, Keisuke Nakamura, Randy Gomez, Ravin Balakrishnan, Pourang Irani

Research Collection School Of Computing and Information Systems

Selecting an item of interest on smartwatches can be tedious and time-consuming as it involves a series of swipe and tap actions. We present PageFlip, a novel method that combines into a single action multiple touch operations such as command invocation and value selection for efficient interaction on smartwatches. PageFlip operates with a page flip gesture that starts by dragging the UI from a corner of the device. We first design PageFlip by examining its key design factors such as corners, drag directions and drag distances. We next compare PageFlip to a functionally equivalent radial menu and a standard swipe …

Criteria-Based Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo

Research Collection School Of Computing and Information Systems

We present a new type of public-key encryption called Criteria-based Encryption (or , for short). Different from Attribute-based Encryption, in , we consider the access policies as criteria carrying different weights. A user must hold some cases (or answers) satisfying the criteria and have sufficient weights in order to successfully decrypt a message. We then propose two Schemes under different settings: the first scheme requires a user to have at least one case for a criterion specified by the encryptor in the access structure, while the second scheme requires a user to have all the cases for each criterion. We …

Demo Abstract: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve battery-less wearable system. Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts, which suggests that energy harvesting and sensing can be performed concurrently. However, realization of simultaneous energy harvesting and sensing (SEHS) is challenging as the energy harvesting process distorts the sensing signal. In this demo, we propose a novel SEHS architecture prototyped in the form factor of an insole, which combines …