OS and Networks Commons^™

Intent Classification Of Short-Text On Social Media, Hemant Purohit, Guozhu Dong, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis …

Randomized Algorithms For Approximating A Connected Dominating Set In Wireless Sensor Networks, Akshaye Dhawan, Michelle Tanco, Aaron Yeiser

Mathematics, Computer Science & Statistics Faculty Publications

A Connected Dominating Set (CDS) of a graph representing a Wireless Sensor Network can be used as a virtual backbone for routing through the network. Since the sensors in the network are constrained by limited battery life, we desire a minimal CDS for the network, a known NP-hard problem. In this paper we present three randomized algorithms for constructing a CDS. We evaluate our algorithms using simulations and compare them to the two-hop K2 algorithm and two other greedy algorithms from the literature. After pruning, the randomized algorithms construct a CDS that are generally equivalent in size to those constructed …

A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin

Research Collection School Of Computing and Information Systems

The multinomial logit (MNL) model is often used for analyzing route choices in real networks in spite of the fact that path utilities are believed to be correlated. Yet, statistical tests for model misspecification are rarely used. This paper shows how the information matrix test for model misspecification proposed byWhite (1982) can be applied to test path-based and link-based MNL route choice models.We present a Monte Carlo experiment using simulated data to assess the size and the power of the test and to compare its performance with the IIA (Hausman and McFadden, 1984) and McFadden–Train Lagrange multiplier (McFadden and Train, …

Adaptive Scaling Of Cluster Boundaries For Large-Scale Social Media Data Clustering, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the …

Salad: A Multimodal Approach For Contextual Video Advertising, Chen Xiang, Tam Nguyen, Mohan Kankanhalli

Computer Science Faculty Publications

The explosive growth of multimedia data on Internet has created huge opportunities for online video advertising. In this paper, we propose a novel advertising technique called SalAd, which utilizes textual information, visual content and the webpage saliency, to automatically associate the most suitable companion ads with online videos. Unlike most existing approaches that only focus on selecting the most relevant ads, SalAd further considers the saliency of selected ads to reduce intentional ignorance. SalAd consists of three basic steps. Given an online video and a set of advertisements, we first roughly identify a set of relevant ads based on the …

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 …

Evaluating The Intrinsic Similarity Between Neural Networks, Stephen Charles Ashmore

Graduate Theses and Dissertations

We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a black box, because neural networks contain complex model surface determined by their weights that combine attributes non-linearly. Two networks that make similar predictions on training data may still generalize differently. FBA enables a diversity of applications, including visualization and canonicalization of neural networks, ensembles, and cross-over between unrelated neural networks in evolutionary optimization. We describe the FBA algorithm, and describe implementations for three applications: genetic algorithms, visualization, and ensembles. We demonstrate FBA's usefulness by comparing a …

Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri

Research Collection School Of Computing and Information Systems

The increasing popularity of using mobile devices in a work context, has led to the need to be able to support more powerful computation. Users no longer remain in an office or at home to conduct their activities, preferring libraries and cafes. In this paper, we consider a mobile cloud computing scenario in which users bring their own mobile devices and are offered a variety of equipment, e.g., desktop computer, smart- TV, or projector, to migrate their applications to, so as to save battery life, improve usability and performance. We formulate a stochastic optimization problem to optimize the allocation of …

Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Using feedback signals from the environment, a reinforcement learning (RL) system typically discovers action policies that recommend actions effective to the states based on a Q-value function. However, uncertainties over the estimation of the Q-values can delay the convergence of RL. For fast RL convergence by accounting for such uncertainties, this paper proposes several enhancements to the estimation and learning of the Q-value using a self-organizing neural network. Specifically, a temporal difference method known as Q-learning is complemented by a Q-value Polarization procedure, which contrasts the Q-values using feedback signals on the effect of the recommended actions. The polarized Q-values …

Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved …

Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han

Research Collection School Of Computing and Information Systems

Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on …

Feedback-Driven Radiology Exam Report Retrieval With Semantics, Sarasi Lalithsena, Luis Tari, Anna Von Reden, Benjamin Wilson, Brian J. Kolowitz, John Kalafut, Steven Gustafson, Amit P. Sheth

Kno.e.sis Publications

Clinical documents are vital resources for radiologists to have a better understanding of patient history. The use of clinical documents can complement the often brief reasons for exams that are provided by physicians in order to perform more informed diagnoses. With the large number of study exams that radiologists have to perform on a daily basis, it becomes too time-consuming for radiologists to sift through each patient's clinical documents. It is therefore important to provide a capability that can present contextually relevant clinical documents, and at the same time satisfy the diverse information needs among radiologists from different specialties. In …

Social Health Signals, Ashutosh Sopan Jadhav, Swapnil Soni, Amit P. Sheth

Kno.e.sis Publications

Recently Twitter, has emerged as one of the primary medium for sharing and seeking of the latest information related to variety of the topics including health information. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, identification of useful information from the deluge of tweets is one of the major challenge. Twitter search is limited to keyword based techniques to retrieve information for a given query and sometimes the results do not contain real-time information. Moreover, …

Implicit Information Extraction From Clinical Notes, Sujan Perera

Kno.e.sis Publications

We address the problem of extracting implicit information from the unstructured clinical notes. Here we introduce the problem of 'implicit entity recognition in clinical notes', propose a knowledge driven approach to address this problem and demonstrate the results of our initial experiments.

Ezdi's Semantics-Enhanced Linguistic, Nlp, And Ml Approach For Health Informatics, Raxit Goswami, Neil Shah, Amit P. Sheth

Kno.e.sis Publications

ezDI uses large and extensive knowledge graph to enhance linguistics, NLP and ML techniques to improve structured data extraction from millions of EMR records. It then normalizes it, and maps it with various computer-processable nomenclature such as SNOMED-CT, RxNorm, ICD-9, ICD-10, CPT, and LOINC. Furthermore, it applies advanced reasoning that exploited domain-specific and hierarchical relationships among entities in the knowledge graph to make the data actionable. These capabilities are part of its highly scalable AWS deployed heath intelligence platform that support healthcare informatics applications, including Computer Assisted Coding (CAC), Computerized Document Improvement (CDI), compliance and audit, and core measures and …

Endogenous Network Effects, Platform Pricing And Market Liquidity, Mei Lin, Ruhai Wu, Wen Zhou

Research Collection School Of Computing and Information Systems

This paper examines a monopoly platform's two-sided pricing strategies in a setting with seller competition, which gives rise to not only positive cross-side network effects between buyers and sellers, but also a negative same-side network effect among sellers. We show that platform pricing depends crucially on the characteristics associated with market liquidity, which contrasts with the previous studies that point to the two sides' relative demand elasticities and/or network effects. A market is said to be more liquid when it has less friction, resulting in a larger total surplus for the platform economy and hence greater equilibrium entry on both …

Information Diffusion, Facebook Clusters, And The Simplicial Model Of Social Aggregation: A Computational Simulation Of Simplicial Diffusers For Community Health Interventions, Kerk Kee, Lisa Sparks, Daniele C. Struppa, Mirco A. Manucci, Alberto Damiano

Communication Faculty Articles and Research

By integrating the simplicial model of social aggregation with existing research on opinion leadership and diffusion networks, this article introduces the constructs of simplicial diffusers (mathematically defined as nodes embedded in simplexes; a simplex is a socially bonded cluster) and simplicial diffusing sets (mathematically defined as minimal covers of a simplicial complex; a simplicial complex is a social aggregation in which socially bonded clusters are embedded) to propose a strategic approach for information diffusion of cancer screenings as a health intervention on Facebook for community cancer prevention and control. This approach is novel in its incorporation of interpersonally bonded clusters, …

Automatic Emotion Identification From Text, Wenbo Wang

Kno.e.sis Publications

Emotions are both prevalent in and essential to most aspects of our lives. They in- fluence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people’s emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that …

Creating Volatility Support For Freebsd, Elyse Bond

LSU New Orleans Theses and Dissertations

Digital forensics is the investigation and recovery of data from digital hardware. The field has grown in recent years to include support for operating systems such as Windows, Linux and Mac OS X. However, little to no support has been provided for less well known systems such as the FreeBSD operating system.

The project presented in this paper focuses on creating the foundational support for FreeBSD via Volatility, a leading forensic tool in the digital forensic community. The kernel and source code for FreeBSD were studied to understand how to recover various data from analysis of a given system’s memory …

Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato

Research Collection School Of Computing and Information Systems

Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized leaerning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding …

Scalable Euclidean Embedding For Big Data, Zohreh S. Alavi, Sagar Sharma, Lu Zhou, Keke Chen

Kno.e.sis Publications

Euclidean embedding algorithms transform data defined in an arbitrary metric space to the Euclidean space, which is critical to many visualization techniques. At big-data scale, these algorithms need to be scalable to massive dataparallel infrastructures. Designing such scalable algorithms and understanding the factors affecting the algorithms are important research problems for visually analyzing big data. We propose a framework that extends the existing Euclidean embedding algorithms to scalable ones. Specifically, it decomposes an existing algorithm into naturally parallel components and non-parallelizable components. Then, data parallel implementations such as MapReduce and data reduction techniques are applied to the two categories of …

An Apache Hadoop Framework For Large-Scale Peptide Identification, Harinivesh Donepudi

Masters Theses & Specialist Projects

Peptide identification is an essential step in protein identification, and Peptide Spectrum Match (PSM) data set is huge, which is a time consuming process to work on a single machine. In a typical run of the peptide identification method, PSMs are positioned by a cross correlation, a statistical score, or a likelihood that the match between the trial and hypothetical is correct and unique. This process takes a long time to execute, and there is a demand for an increase in performance to handle large peptide data sets. Development of distributed frameworks are needed to reduce the processing time, but …

Evaluating A Potential Commercial Tool For Healthcare Application For People With Dementia, Tanvi Banerjee, Pramod Anantharam, William L. Romine, Larry Wayne Lawhorne

Kno.e.sis Publications

The widespread use of smartphones and sensors has made physiology, environment, and public health notifications amenable to continuous monitoring. Personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context, converting relevant medical knowledge into actionable information for better and timely decisions. We apply these principles in the healthcare domain of dementia. Specifically, in this study we validate one of our sensor platforms to ascertain whether it will be suitable for detecting physiological changes that may help us detect changes in people with dementia. This study shows …

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 …

Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni

Kno.e.sis Publications

With the advent of the web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these online services to share and seek health real-time information has in- creased exponentially. OHIS use web search engines or microblogging search services to seek out latest, relevant as well as reliable health in- formation. When OHIS turn to microblogging search services to search real-time content, trends and breaking news, etc. the search results are not promising. Two major challenges exist in the current microblogging search engines are keyword based techniques and results do not contain real-time information. To address these challenges, …

"Time For Dabs": Analyzing Twitter Data On Butane Hash Oil Use, Raminta Daniulaityte, Robert G. Carlson, Farahnaz Golroo, Sanjaya Wijeratne, Edward W. Boyer, Silvia S. Martins, Ramzi W. Nahhas, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Trust Management: Multimodal Data Perspective, Krishnaprasad Thirunarayan

Kno.e.sis Publications

No abstract provided.

Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter

Research Collection School Of Computing and Information Systems

We explore the problem of placing object replicas on nodes in a distributed system to maximize the number of objects that remain available when node failures occur. In our model, failing (the nodes hosting) a given threshold of replicas is sufficient to disable each object, and the adversary selects which nodes to fail to minimize the number of objects that remain available. We specifically explore placement strategies based on combinatorial structures called t-packings; provide a lower bound for the object availability they offer; show that these placements offer availability that is c-competitive with optimal; propose an efficient algorithm for computing …

Entity Recommendations Using Hierarchical Knowledge Bases, Siva Kumar Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit P. Sheth

Kno.e.sis Publications

Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may or better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may be utilized for recommendation systems. We propose a content-based recommendation approaches that adapts a spreading activation algorithm over the DBpedia category structure to identify entities of interest to the user. Evaluation of the algorithm over the Movielens dataset demonstrates that our method yields …

Tracking Criminals On Facebook: A Case Study From A Digital Forensics Reu Program, Daniel Weiss, Gary Warner

Annual ADFSL Conference on Digital Forensics, Security and Law

The 2014 Digital Forensics Research Experience for Undergraduates (REU) Program at the University of Alabama at Birmingham (UAB) focused its summer efforts on tracking criminal forums and Facebook groups. The UAB-REU Facebook team was provided with a list of about 60 known criminal groups on Facebook, with a goal to track illegal information posted in these groups and ultimately store the information in a searchable database for use by digital forensic analysts. Over the course of about eight weeks, the UAB-REU Facebook team created a database with over 400 Facebook groups conducting criminal activity along with over 100,000 unique users …