Databases and Information Systems Commons^™

Compressive Representation For Device-Free Activity Recognition With Passive Rfid Signal Strength, Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, Wenjie Ruan

Research Collection School Of Computing and Information Systems

Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system …

Two Birds With One Stone: Classifying Positive And Unlabeled Examples On Uncertain Data Streams, Donghong Han, Shuoru Li, Fulin Wei, Yuying Tang, Feida Zhu, Guoren Wang

Research Collection School Of Computing and Information Systems

An important feature characteristic of the data streams in many of today's big data applications is the intrinsic uncertainty, which could happen for both item occurrence and attribute value. While this has already posed great challenges for fundamental data mining tasks such as classification, things are made even more complicated by the fact that completely-labeled examples are usually unavailable in such settings, leaving researchers the only option to learn classifiers on partially-labeled examples on uncertain data streams. Furthermore, there will be concept drift on evolving data streams. To address these challenges, this paper therefore focuses on the study of learning …

Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu

Research Collection School Of Computing and Information Systems

The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE …

Sequential Recommendation With User Memory Networks, Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, Hongyuan Zha

Research Collection School Of Computing and Information Systems

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design …

Enhanced Vireo Kis At Vbs 2018, Phuong Anh Nguyen, Yi-Jie Lu, Hao Zhang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The VIREO Known-Item Search (KIS) system has joined the Video Browser Showdown (VBS) [1] evaluation benchmark for the first time in year 2017. With experiences learned, the second version of VIREO KIS is presented in this paper. Considering the color-sketch based retrieval, we propose a simple grid-based approach for color query. This method allows the aggregation of color distributions in video frames into a shot representation, and generates the pre-computed rank list for all available queries which reduces computational resources and favors a recommendation module. With focusing on concept based retrieval, we modify our multimedia event detection system at TRECVID …

Food Photo Recognition For Dietary Tracking: System And Experiment, Zhao-Yan Ming, Jingjing Chen, Yu Cao, Ciarán Forde, Chong-Wah Ngo, Tat Seng Chua

Research Collection School Of Computing and Information Systems

Tracking dietary intake is an important task for health management especially for chronic diseases such as obesity, diabetes, and cardiovascular diseases. Given the popularity of personal hand-held devices, mobile applications provide a promising low-cost solution to tackle the key risk factor by diet monitoring. In this work, we propose a photo based dietary tracking system that employs deep-based image recognition algorithms to recognize food and analyze nutrition. The system is beneficial for patients to manage their dietary and nutrition intake, and for the medical institutions to intervene and treat the chronic diseases. To the best of our knowledge, there are …

Iot-Enhanced Human Experience, Amit P. Sheth, Biplav Srivastava, Florian Michahelles

Kno.e.sis Publications

The two articles in this special section represent ongoing Internet of Things applications in the context of Europe trying to make solutions usable to people in daily times.

Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes

Faculty Publications

Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the …

Support Vector Machines For Image Spam Analysis, Aneri Chavda, Katerina Potika, Fabio Di Troia, Mark Stamp

Faculty Publications, Computer Science

Email is one of the most common forms of digital communication. Spam is unsolicited bulk email, while image spam consists of spam text embedded inside an image. Image spam is used as a means to evade text-based spam filters, and hence image spam poses a threat to email-based communication. In this research, we analyze image spam detection using support vector machines (SVMs), which we train on a wide variety of image features. We use a linear SVM to quantify the relative importance of the features under consideration. We also develop and analyze a realistic “challenge” dataset that illustrates the limitations …

Data Warehousing Class Project Report, Gaya Haciane, Chuan Chieh Lu, Rassaniya Lerdphayakkarat, Rudraxi Mitra

Engineering and Technology Management Student Projects

Data mining is widely described or defined as the discipline of: “making sense of the data”. In today’s day and age, the rise of ubiquity of information calls for more advanced and developed techniques to mine the data and come up with insights. Data mining finds applications in many different fields and industries: Whether it is in Embryology, Crops, Elections, or Business Marketing...etc. It is not a wild assumption to consider that every organization in the world has some data mining capabilities or its main activity necessitates it and they have some third party organization doing that for them. One …

Knowledge-Enabled Personalized Dashboard For Asthma Management In Children, Vaikunth Sridharan, Revathy Venkataramanan, Dipesh Kadariya, Krishnaprasad Thirunarayan, Amit Sheth, Maninder Kalra

Kno.e.sis Publications

Introduction: Childhood Asthma is a significant public health concern worldwide. Effective management of childhood asthma requires close monitoring of disease triggers, medication compliance and symptom control. The recent growth of the Internet of Things (IoT) based devices has enabled continuous monitoring of patients. kHealth-Asthma is a knowledge-enabled semantic framework consisting of IoT enabled sensors to record patient symptoms, medication usage and their environment. For each patient, 29 diverse parameters with 1852 data points are collected daily. kHealthDash platform enables real-time visual analysis at an individual and cohort level over such high volume, high variety data.

Methods: The kHealth kit was …

Khealth: A Personalized Healthcare Approach For Pediatric Asthma, Utkarshani Jaimini, Hong Y. Yip, Revathy Venkataramanan, Dipesh Kadariya, Vaikunth Sridharan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra, Amit Sheth

Kno.e.sis Publications

Can we assess the asthma control level, determine vulnerability, and medication compliance for a patient? Can we understand the causal relationship between the asthma symptom and possible factors responsible for it? Can we reduce the number of asthma attacks through continuous monitoring of the patient’s health condition?

Poster: Image Disguising For Privacy-Preserving Deep Learning, Sagar Sharma, Keke Chen

Kno.e.sis Publications

No abstract provided.

“How Is My Child’S Asthma?” Digital Phenotype And Actionable Insights For Pediatric Asthma, Utkarshani Jaimini, Krishnaprasad Thirunarayan, Maninder Kalra, Revathy Venkataramanan, Dipesh Kadariya, Amit Sheth

Kno.e.sis Publications

Background: In the traditional asthma management protocol, a child meets with a clinician infrequently, once in 3 to 6 months, and is assessed using the Asthma Control Test questionnaire. This information is inadequate for timely determination of asthma control, compliance, precise diagnosis of the cause, and assessing the effectiveness of the treatment plan. The continuous monitoring and improved tracking of the child’s symptoms, activities, sleep, and treatment adherence can allow precise determination of asthma triggers and a reliable assessment of medication compliance and effectiveness. Digital phenotyping refers to moment-by-moment quantification of the individual-level human phenotype in situ using data from …

An Attribute Agreement Method For Hfacs Inter-Rater Reliability Assessment, Teddy Steven Cotter, Veysel Yesilbas

Engineering Management & Systems Engineering Faculty Publications

Inter-rater reliability can be regarded as the degree of agreement among raters on a given item or a circumstance. Multiple approaches have been taken to estimate and improve inter-rater reliability of the United States Department of Defense Human Factors Analysis and Classification System used by trained accident investigators. In this study, three trained instructor pilots used the DoD-HFACS to classify 347 U.S. Air Force Accident Investigation Board (AIB) Class-A reports between the years of 2000 and 2013. The overall method consisted of four steps: (1) train on HFACS definitions, (2) verify rating reliability, (3) rate HFACS reports, and (4) random …

Leadership Skills To Sustain High-Tech Entrepreneurial Ventures, Zoaib Z. Rangwala

Walden Dissertations and Doctoral Studies

High-tech (HT) innovation-oriented entrepreneurs start 35% more ventures and create 10% more jobs in the first 5 years of operation than the rest of the private sector and drive significant economic growth across all industries; however, more than 50% of the entrepreneurial HT ventures fail during the first 5 years of operations. Guided by the conceptual framework of transformational leadership theory, the purpose of this multicase study was to explore skills used by successful entrepreneurial leaders to sustain their HT ventures in Silicon Valley, California. Data collection was from 8 participants in semistructured 1-on-1 interviews and 3 participants in a …

Exploring Sme Vulnerabilities To Cyber-Criminal Activities Through Employee Behavior And Internet Access, Jerry Allen Twisdale

Walden Dissertations and Doctoral Studies

Cybercriminal activity may be a relatively new concern to small and medium enterprises (SMEs), but it has the potential to create financial and liability issues for SME organizations. The problem is that SMEs are a future growth target for cybercrime activity as larger corporations begin to address security issues to reduce cybercriminal risks and vulnerabilities. The purpose of this study was to explore a small business owner's knowledge about to the principal elements of decision making for SME investment into cybersecurity education for employees with respect to internet access and employee vulnerabilities. The theoretical framework consisted of the psychological studies …

Patient Satisfaction Management In Office Visits And Telehealth In Health Care Technology, Todd Price

Walden Dissertations and Doctoral Studies

Telehealth and remote medical treatments have begun to be more commonly used in healthcare systems. Researchers have theorized that providers' abilities to treat patients are not directly tied to the proximity of the patient to the doctor, but by the identification and treatment of the patient's symptoms. Although the treatment and cure rates are being established within individual health systems and professional medical associations, empirical research is lacking regarding patient satisfaction with this remote treatment situation. The purpose of this quantitative study was to address this gap by examining satisfaction ratings of patients between virtual provider visits and face-to-face provider …

A Study Of Groupthink In Project Teams, John Reaves

Walden Dissertations and Doctoral Studies

Project teams advance a common goal by working together on projects that require a diverse set of skills and are difficult for 1 person to complete. In this study, there was an exploration of the antecedents to groupthink in project teams from the perspectives of project managers. Many companies use project managers to complete critical objectives; avoiding groupthink is crucial to their success. The purpose of this research was to understand why project teams are susceptible to groupthink and what precautions managers can take to avoid it. The conceptual framework utilized in this study was Janis' concept of groupthink, which …

Strategies For Healthcare Payer Information Technology Integration After Mergers And Acquisitions, Kishore Maranganti

Walden Dissertations and Doctoral Studies

Despite the high rate of failure in merger and acquisition (M&A) transactions, many organizations continue to rely on M&As as their primary growth strategy and to address market competition. The purpose of this qualitative single case study was to explore strategies managers from a large healthcare payer in the midwestern United States used to achieve operational and strategic synergies during the postacquisition information technology (IT) integration phase. Haspeslagh and Jemison's acquisition integration approaches model was the conceptual framework for the study. Methodological triangulation was established by analyzing the data from the semistructured interviews of 6 senior executives and 6 IT …