VTechWorks Repository :: Browsing by Author "Wang, Gang Alan"

Browsing by Author "Wang, Gang Alan"

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An Application-Attuned Framework for Optimizing HPC Storage Systems
Paul, Arnab Kumar (Virginia Tech, 2020-08-19)
High performance computing (HPC) is routinely employed in diverse domains such as life sciences, and Geology, to simulate and understand the behavior of complex phenomena. Big data driven scientific simulations are resource intensive and require both computing and I/O capabilities at scale. There is a crucial need for revisiting the HPC I/O subsystem to better optimize for and manage the increased pressure on the underlying storage systems from big data processing. Extant HPC storage systems are designed and tuned for a specific set of applications targeting a range of workload characteristics, but they lack the flexibility in adapting to the ever-changing application behaviors. The complex nature of modern HPC storage systems along with the ever-changing application behaviors present unique opportunities and engineering challenges. In this dissertation, we design and develop a framework for optimizing HPC storage systems by making them application-attuned. We select three different kinds of HPC storage systems - in-memory data analytics frameworks, parallel file systems and object storage. We first analyze the HPC application I/O behavior by studying real-world I/O traces. Next we optimize parallelism for applications running in-memory, then we design data management techniques for HPC storage systems, and finally focus on low-level I/O load balance for improving the efficiency of modern HPC storage systems.
Automatic Internet of Things Device Category Identification using Traffic Rates
Hsu, Alexander Sirui (Virginia Tech, 2019-03-12)
Due to the ever increasing supply of new Internet of Things (IoT) devices being added onto a network, it is vital secure the devices from incoming cyber threats. The manufacturing process of creating and developing a new IoT device allows many new companies to come out with their own device. These devices also increase the network risk because many IoT devices are created without proper security implementation. Utilizing traffic patterns as a method of device type detection will allow behavior identification using only Internet Protocol (IP) header information. The network traffic captured from 20 IoT devices belonging to 4 distinct types (IP camera, on/off switch, motion sensor, and temperature sensor) are generalized and used to identify new devices previously unseen on the network. Our results indicate some categories have patterns that are easier to generalize, while other categories are harder but we are still able recognize some unique characteristics. We also are able to deploy this in a test production network and adapted previous methods to handle streaming traffic and an additional noise categorization capable of identify non-IoT devices. The performance of our model is varied between classes, signifying that much future work has to be done to increase the classification score and overall usefulness.
Characterizing and Detecting Online Deception via Data-Driven Methods
Hu, Hang (Virginia Tech, 2020-05-27)
In recent years, online deception has become a major threat to information security. Online deception that caused significant consequences is usually spear phishing. Spear-phishing emails come in a very small volume, target a small number of audiences, sometimes impersonate a trusted entity and use very specific content to redirect targets to a phishing website, where the attacker tricks targets sharing their credentials. In this thesis, we aim at measuring the entire process. Starting from phishing emails, we examine anti-spoofing protocols, analyze email services' policies and warnings towards spoofing emails, and measure the email tracking ecosystem. With phishing websites, we implement a powerful tool to detect domain name impersonation and detect phishing pages using dynamic and static analysis. We also analyze credential sharing on phishing websites, and measure what happens after victims share their credentials. Finally, we discuss potential phishing and privacy concerns on new platforms such as Alexa and Google Assistant. In the first part of this thesis (Chapter 3), we focus on measuring how email providers detect and handle forged emails. We also try to understand how forged emails can reach user inboxes by deliberately composing emails. Finally, we check how email providers warn users about forged emails. In the second part (Chapter 4), we measure the adoption of anti-spoofing protocols and seek to understand the reasons behind the low adoption rates. In the third part of this thesis (Chapter 5), we observe that a lot of phishing emails use email tracking techniques to track targets. We collect a large dataset of email messages using disposable email services and measure the landscape of email tracking. In the fourth part of this thesis (Chapter 6), we move on to phishing websites. We implement a powerful tool to detect squatting domains and train a machine learning model to classify phishing websites. In the fifth part (Chapter 7), we focus on the credential leaks. More specifically, we measure what happens after the targets' credentials are leaked. We monitor and measure the potential post-phishing exploiting activities. Finally, with new voice platforms such as Alexa becoming more and more popular, we wonder if new phishing and privacy concerns emerge with new platforms. In this part (Chapter 8), we systematically assess the attack surfaces by measuring sensitive applications on voice assistant systems. My thesis measures important parts of the complete process of online deception. With deeper understandings of phishing attacks, more complete and effective defense mechanisms can be developed to mitigate attacks in various dimensions.
Collaborative learning in Open Source Software (OSS) communities: The dynamics and challenges in networked learning environments
Mitra, Raktim (Virginia Tech, 2011-06-14)
The proliferation of web based technologies has resulted in new forms of communities and organizations with enormous implications for design of learning and education. This thesis explores learning occurring within open source software (OSS) communities. OSS communities are a dominant form of organizing in software development with implications not only for innovative product development but also for the training of a large number of software developers. The central catalyst of learning within these communities is expert-novice interactions. These interactions between experts and novices or newcomers are critical for the growth and sustenance of a community and therefore it is imperative that experts are able to provide newcomers requisite advice and support as they traverse the community and develop software. Although prior literature has demonstrated the significance of expert-novice interactions, there are two central issues that have not been examined. First, there is no examination of the role of external events on community interaction, particularly as it relates to experts and novices. Second, the exact nature of expert help, particularly, the quantity of help and whether it helps or hinders newcomer participation has not been studied. This thesis studies these two aspects of expert-novice interaction within OSS communities. The data for this study comes from two OSS communities. The Java newcomer forum was studied as it provided a useful setting for examining external events given the recent changes in Java's ownership. Furthermore, the forum has a rating system which classifies newcomers and experienced members allowing the analysis of expert-novice interactions. The second set of data comes from the MySQL newcomer forum which has also undergone organizational changes and allows for comparison with data from the Java forum. Data were collected by parsing information from the HTML pages and stored in a relational database. To analyze the effect of external events, a natural experiment method was used whereby participation levels were studied around significant events that affected the community. To better understand the changes contextually, an extensive study of major news outlets was also undertaken. Findings from the external event study show significant changes in participation patterns, especially among newcomers in response to key external events. The study also revealed that the changes in participation of newcomers were observed even though other internal characteristics (help giving, expert participation) did not change indicating that external events have a strong bearing on community participation. The effect of expert advice was studied using a logistic regression model to determine how specific participation patterns in discussion threads led to the final response to newcomers. This was supported by social network analysis to visually interpret the participation patterns of experienced members in two different scenarios, one in which the question was answered and the other where it was not. Findings show that higher number of responses from experienced members did not correlate with a response. Therefore, although expert help is essential, non-moderated or unguided help can lead to conflict among experts and inefficient feedback to newcomers.
Consumer-Centric Innovation for Mobile Apps Empowered by Social Media Analytics
Qiao, Zhilei (Virginia Tech, 2018-06-20)
Due to the rapid development of Internet communication technologies (ICTs), an increasing number of social media platforms exist where consumers can exchange comments online about products and services that businesses offer. The existing literature has demonstrated that online user-generated content can significantly influence consumer behavior and increase sales. However, its impact on organizational operations has been primarily focused on marketing, with other areas understudied. Hence, there is a pressing need to design a research framework that explores the impact of online user-generated content on important organizational operations such as product innovation, customer relationship management, and operations management. Research efforts in this dissertation center on exploring the co-creation value of online consumer reviews, where consumers' demands influence firms' decision-making. The dissertation is composed of three studies. The first study finds empirical evidence that quality signals in online product reviews are predictors of the timing of firms' incremental innovation. Guided by the product differentiation theory, the second study examines how companies' innovation and marketing differentiation strategies influence app performance. The last study proposes a novel text analytics framework to discover different information types from user reviews. The research contributes theoretical and practical insights to consumer-centric innovation and social media analytics literature.
Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation
Burch, Zachary Campbell (Virginia Tech, 2018-10-29)
Credential theft is a network intrusion vector that subverts traditional defenses of a campus network, with a malicious login being the act of an attacker using those stolen credentials to access the target network. Historically, this approach is simple for an attacker to conduct and hard for a defender to detect. Alternative mitigation strategies require an in depth view of the network hosts, an untenable proposition in a campus network. We introduce a method of spatial augmentation of login events, creating a user and source IP trajectory for each event. These location mappings, built using user wireless activity and network state information, provide features needed for login classification. From this, we design and build a real time data collection, augmentation, and classification system for generating alerts on malicious events. With a relational database for data processing and a trained weighted random forests ensemble classifier, generated alerts are both timely and few enough to allow human analyst review of all generated events. We evaluate this design for three levels of attacker ability with a defined threat model. We evaluate our approach with a proof of concept system on weeks of live data collected from the Virginia Tech campus, under an IRB approved research protocol.
Deceptive Environments for Cybersecurity Defense on Low-power Devices
Kedrowitsch, Alexander Lee (Virginia Tech, 2017-06-05)
The ever-evolving nature of botnets have made constant malware collection an absolute necessity for security researchers in order to analyze and investigate the latest, nefarious means by which bots exploit their targets and operate in concert with each other and their bot master. In that effort of on-going data collection, honeypots have established themselves as a curious and useful tool for deception-based security. Low-powered devices, such as the Raspberry Pi, have found a natural home with some categories of honeypots and are being embraced by the honeypot community. Due to the low cost of these devices, new techniques are being explored to employ multiple honeypots within a network to act as sensors, collecting activity reports and captured malicious binaries to back-end servers for later analysis and network threat assessments. While these techniques are just beginning to gain their stride within the security community, they are held back due to the minimal amount of deception a traditional honeypot on a low-powered device is capable of delivering. This thesis seeks to make a preliminary investigation into the viability of using Linux containers to greatly expand the deception possible on low-powered devices by providing isolation and containment of full system images with minimal resource overhead. It is argued that employing Linux containers on low-powered device honeypots enables an entire category of honeypots previously unavailable on such hardware platforms. In addition to granting previously unavailable interaction with honeypots on Raspberry Pis, the use of Linux containers grants unique advantages that have not previously been explored by security researchers, such as the ability to defeat many types of virtual environment and monitoring tool detection methods.
Defending Against GPS Spoofing by Analyzing Visual Cues
Xu, Chao (Virginia Tech, 2020-05-21)
Massive GPS navigation services are used by billions of people in their daily lives. GPS spoofing is quite a challenging problem nowadays. Existing Anti-GPS spoofing systems primarily focus on expensive equipment and complicated algorithms, which are not practical and deployable for most of the users. In this thesis, we explore the feasibility of a simple text-based system design for Anti-GPS spoofing. The goal is to use the lower cost and make the system more effective and robust for general spoofing attack detection. Our key idea is to only use the textual information from the physical world and build a real-time system to detect GPS spoofing. To demonstrate the feasibility, we first design image processing modules to collect sufficient textual information in panoramic images. Then, we simulate real-world spoofing attacks from two cities to build our training and testing datasets. We utilize LSTM to build a binary classifier which is the key for our Anti-GPS spoofing system. Finally, we evaluate the system performance by simulating driving tests. We prove that our system can achieve more than 98% detection accuracy when the ratio of attacked points in a driving route is more than 50%. Our system has a promising performance for general spoofing attack strategies and it proves the feasibility of using textual information for the spoofing attack detection.
Detecting Bots using Stream-based System with Data Synthesis
Hu, Tianrui (Virginia Tech, 2020-05-28)
Machine learning has shown great success in building security applications including bot detection. However, many machine learning models are difficult to deploy since model training requires the continuous supply of representative labeled data, which are expensive and time-consuming to obtain in practice. In this thesis, we build a bot detection system with a data synthesis method to explore detecting bots with limited data to address this problem. We collected the network traffic from 3 online services in three different months within a year (23 million network requests). We develop a novel stream-based feature encoding scheme to support our model to perform real-time bot detection on anonymized network data. We propose a data synthesis method to synthesize unseen (or future) bot behavior distributions to enable our system to detect bots with extremely limited labeled data. The synthesis method is distribution-aware, using two different generators in a Generative Adversarial Network to synthesize data for the clustered regions and the outlier regions in the feature space. We evaluate this idea and show our method can train a model that outperforms existing methods with only 1% of the labeled data. We show that data synthesis also improves the model's sustainability over time and speeds up the retraining. Finally, we compare data synthesis and adversarial retraining and show they can work complementary with each other to improve the model generalizability.
Detecting Malicious Landing Pages in Malware Distribution Networks
Wang, Gang Alan; Stokes, Jack W.; Herley, Cormac; Felstead, David (IEEE, 2013-06)
Drive-by download attacks attempt to compromise a victim’s computer through browser vulnerabilities. Often they are launched from Malware Distribution Networks (MDNs) consisting of landing pages to attract traffic, intermediate redirection servers, and exploit servers which attempt the compromise. In this paper, we present a novel approach to discovering the landing pages that lead to drive-by downloads. Starting from partial knowledge of a given collection of MDNs we identify the malicious content on their landing pages using multiclass feature selection. We then query the webpage cache of a commercial search engine to identify landing pages containing the same or similar content. In this way we are able to identify previously unknown landing pages belonging to already identified MDNs, which allows us to expand our understanding of the MDN. We explore using both a rule-based and classifier approach to identifying potentially malicious landing pages. We build both systems and independently verify using a high-interaction honeypot that the newly identified landing pages indeed attempt drive-by downloads. For the rule-based system 57%of the landing pages predicted as malicious are confirmed, and this success rate remains constant in two large trials spaced five months apart. This extends the known footprint of the MDNs studied by 17%. The classifier-based system is less successful, and we explore possible reasons.
Disruption Information, Network Topology and Supply Chain Resilience
Li, Yuhong (Virginia Tech, 2017-07-17)
This dissertation consists of three essays studying three closely related aspects of supply chain resilience. The first essay is "Value of Supply Disruption Information and Information Accuracy", in which we examine the factors that influence the value of supply disruption information, investigate how information accuracy influences this value, and provide managerial suggestions to practitioners. The study is motivated by the fact that fully accurate disruption information may be difficult and costly to obtain and inaccurate disruption information can decrease the financial benefit of prior knowledge and even lead to negative performance. We perform the analysis by adopting a newsvendor model. The results show that information accuracy, specifically information bias and information variance, plays an important role in determining the value of disruption information. However, this influence varies at different levels of disruption severity and resilience capacity. The second essay is "Quantifying Supply Chain Resilience: A Dynamic Approach", in which we provide a new type of quantitative framework for assessing network resilience. This framework includes three basic elements: robustness, recoverability and resilience, which can be assessed with respect to different performance measures. Then we present a comprehensive analysis on how network structure and other parameters influence these different elements. The results of this analysis clearly show that both researchers and practitioners should be aware of the possible tradeoffs among different aspects of supply chain resilience. The ability of the framework to support better decision making is then illustrated through a systemic analysis based on a real supply chain network. The third essay is "Network Characteristics and Supply Chain Disruption Resilience", in which we investigate the relationships between network characteristics and supply chain resilience. In this work, we first prove that investigating network characteristics can lead to a better understanding of supply chain resilience behaviors. Later we select key characteristics that play a critical role in determining network resilience. We then construct the regression and decision tree models of different supply chain resilience measures, which can be used to estimate supply chain network resilience given the key influential characteristics. Finally, we conduct a case study to examine the estimation accuracy.
Effective Search in Online Knowledge Communities: A Genetic Algorithm Approach
Zhang, Xiaoyu (Virginia Tech, 2009-09-11)
Online Knowledge Communities, also known as online forum, are popular web-based tools that allow members to seek and share knowledge. Documents to answer varieties of questions are associated with the process of knowledge exchange. The social network of members in an Online Knowledge Community is an important factor to improve search precision. However, prior ranking functions don't handle this kind of document with using this information. In this study, we try to resolve the problem of finding authoritative documents for a user query within an Online Knowledge Community. Unlike prior ranking functions which consider either content based feature, hyperlink based feature, or document structure based feature, we explored the Online Knowledge Community social network structure and members social interaction activities to design features that can gauge the two major factors affecting user knowledge adoption decision: argument quality and source credibility. We then design a customized Genetic Algorithm to adjust the weights for new features we proposed. We compared the performance of our ranking strategy with several others baselines on a real world data www.vbcity.com/forums/. The evaluation results demonstrated that our method could improve the user search satisfaction with an obviously percentage. At the end, we concluded that our approach based on knowledge adoption model and Genetic Algorithm is a better ranking strategy in the Online Knowledge Community.
Empirical Analysis of User Passwords across Online Services
Wang, Chun (Virginia Tech, 2018-06-05)
Leaked passwords from data breaches can pose a serious threat if users reuse or slightly modify the passwords for other services. With more and more online services getting breached today, there is still a lack of large-scale quantitative understanding of the risks of password reuse and modification. In this project, we perform the first large-scale empirical analysis of password reuse and modification patterns using a ground-truth dataset of 28.8 million users and their 61.5 million passwords in 107 services over 8 years. We find that password reuse and modification is a very common behavior (observed on 52% of the users). More surprisingly, sensitive online services such as shopping websites and email services received the most reused and modified passwords. We also observe that users would still reuse the already-leaked passwords for other online services for years after the initial data breach. Finally, to quantify the security risks, we develop a new training-based guessing algorithm. Extensive evaluations show that more than 16 million password pairs (30% of the modified passwords and all the reused passwords) can be cracked within just 10 guesses. We argue that more proactive mechanisms are needed to protect user accounts after major data breaches.
Ensemble Learning Techniques for Structured and Unstructured Data
King, Michael Allen (Virginia Tech, 2015-04-01)
This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model evaluations. The first research section addresses the consumer demand forecasting and daily capacity management requirements of a nationally recognized alpine ski resort in the state of Utah, in the United States of America. A basic econometric model is developed and three classic predictive models evaluated the effectiveness. These predictive models were subsequently used as input for four ensemble modeling techniques. Ensemble learning techniques are shown to be effective. The second research section discusses the opportunities and challenges faced by a leading firm providing sponsored search marketing services. The goal for sponsored search marketing campaigns is to create advertising campaigns that better attract and motivate a target market to purchase. This research develops a method for classifying profitable campaigns and maximizing overall campaign portfolio profits. Four traditional classifiers are utilized, along with four ensemble learning techniques, to build classifier models to identify profitable pay-per-click campaigns. A MetaCost ensemble configuration, having the ability to integrate unequal classification cost, produced the highest campaign portfolio profit. The third research section addresses the management challenges of online consumer reviews encountered by service industries and addresses how these textual reviews can be used for service improvements. A service improvement framework is introduced that integrates traditional text mining techniques and second order feature derivation with ensemble learning techniques. The concept of GLOW and SMOKE words is introduced and is shown to be an objective text analytic source of service defects or service accolades.
Extracting the Wisdom of Crowds From Crowdsourcing Platforms
Du, Qianzhou (Virginia Tech, 2019-08-02)
Enabled by the wave of online crowdsourcing activities, extracting the Wisdom of Crowds (WoC) has become an emerging research area, one that is used to aggregate judgments, opinions, or predictions from a large group of individuals for improved decision making. However, existing literature mostly focuses on eliciting the wisdom of crowds in an offline context—without tapping into the vast amount of data available on online crowdsourcing platforms. To extract WoC from participants on online platforms, there exist at least three challenges, including social influence, suboptimal aggregation strategies, and data sparsity. This dissertation aims to answer the research question of how to effectively extract WoC from crowdsourcing platforms for the purpose of making better decisions. In the first study, I designed a new opinions aggregation method, Social Crowd IQ (SCIQ), using a time-based decay function to eliminate the impact of social influence on crowd performance. In the second study, I proposed a statistical learning method, CrowdBoosting, instead of a heuristic-based method, to improve the quality of crowd wisdom. In the third study, I designed a new method, Collective Persuasibility, to solve the challenge of data sparsity in a crowdfunding platform by inferring the backers' preferences and persuasibility. My work shows that people can obtain business benefits from crowd wisdom, and it provides several effective methods to extract wisdom from online crowdsourcing platforms, such as StockTwits, Good Judgment Open, and Kickstarter.
A framework for finding and summarizing product defects, and ranking helpful threads from online customer forums through machine learning
Jiao, Jian (Virginia Tech, 2013-06-05)
The Internet has revolutionized the way users share and acquire knowledge. As important and popular Web-based applications, online discussion forums provide interactive platforms for users to exchange information and report problems. With the rapid growth of social networks and an ever increasing number of Internet users, online forums have accumulated a huge amount of valuable user-generated data and have accordingly become a major information source for business intelligence. This study focuses specifically on product defects, which are one of the central concerns of manufacturing companies and service providers, and proposes a machine learning method to automatically detect product defects in the context of online forums. To complement the detection of product defects , we also present a product feature extraction method to summarize defect threads and a thread ranking method to search for troubleshooting solutions. To this end, we collected different data sets to test these methods experimentally and the results of the tests show that our methods are very promising: in fact, in most cases, they outperformed the current state-of-the-art methods.
From Theory to Practice: Deployment-grade Tools and Methodologies for Software Security
Rahaman, Sazzadur (Virginia Tech, 2020-08-25)
Following proper guidelines and recommendations are crucial in software security, which is mostly obstructed by accidental human errors. Automatic screening tools have great potentials to reduce the gap between the theory and the practice. However, the goal of scalable automated code screening is largely hindered by the practical difficulty of reducing false positives without compromising analysis quality. To enable compile-time security checking of cryptographic vulnerabilities, I developed highly precise static analysis tools (CryptoGuard and TaintCrypt) that developers can use routinely. The main technical enabler for CryptoGuard is a set of detection algorithms that refine program slices by leveraging language-specific insights, where TaintCrypt relies on symbolic execution-based path-sensitive analysis to reduce false positives. Both CryptoGuard and TaintCrypt uncovered numerous vulnerabilities in real-world software, which proves the effectiveness. Oracle has implemented our cryptographic code screening algorithms for Java in its internal code analysis platform, Parfait, and detected numerous vulnerabilities that were previously unknown. I also designed a specification language named SpanL to easily express rules for automated code screening. SpanL enables domain experts to create domain-specific security checking. Unfortunately, tools and guidelines are not sufficient to ensure baseline security in internet-wide ecosystems. I found that the lack of proper compliance checking induced a huge gap in the payment card industry (PCI) ecosystem. I showed that none of the PCI scanners (out of 6), we tested are fully compliant with the guidelines, issuing certificates to merchants that still have major vulnerabilities. Consequently, 86% (out of 1,203) of the e-commerce websites we tested, are non-compliant. To improve the testbeds in the light of our work, the PCI Security Council shared a copy of our PCI measurement paper to the dedicated companies that host, manage, and maintain the PCI certification testbeds.
Going Deeper with Images and Natural Language
Ma, Yufeng (Virginia Tech, 2019-03-29)
One aim in the area of artificial intelligence (AI) is to develop a smart agent with high intelligence that is able to perceive and understand the complex visual environment around us. More ambitiously, it should be able to interact with us about its surroundings in natural languages. Thanks to the progress made in deep learning, we've seen huge breakthroughs towards this goal over the last few years. The developments have been extremely rapid in visual recognition, in which machines now can categorize images into multiple classes, and detect various objects within an image, with an ability that is competitive with or even surpasses that of humans. Meanwhile, we also have witnessed similar strides in natural language processing (NLP). It is quite often for us to see that now computers are able to almost perfectly do text classification, machine translation, etc. However, despite much inspiring progress, most of the achievements made are still within one domain, not handling inter-domain situations. The interaction between the visual and textual areas is still quite limited, although there has been progress in image captioning, visual question answering, etc. In this dissertation, we design models and algorithms that enable us to build in-depth connections between images and natural languages, which help us to better understand their inner structures. In particular, first we study how to make machines generate image descriptions that are indistinguishable from ones expressed by humans, which as a result also achieved better quantitative evaluation performance. Second, we devise a novel algorithm for measuring review congruence, which takes an image and review text as input and quantifies the relevance of each sentence to the image. The whole model is trained without any supervised ground truth labels. Finally, we propose a brand new AI task called Image Aspect Mining, to detect visual aspects in images and identify aspect level rating within the review context. On the theoretical side, this research contributes to multiple research areas in Computer Vision (CV), Natural Language Processing (NLP), interactions between CVandNLP, and Deep Learning. Regarding impact, these techniques will benefit related users such as the visually impaired, customers reading reviews, merchants, and AI researchers in general.
The Human Factor in Supply Chain Risk Management
Kwaramba, Shingirai C. (Virginia Tech, 2019-02-04)
In a three paper essay series we address the human impact in SCRM from the microeconomic and macroeconomic perspectives. First, using a positivist theory building approach, we synthesize behavioral risk management and supply chain risk management theory to propose behavioral supply chain risk management as a new topic area. This paper is microeconomic in nature and focuses mostly on individuals as the unit of analysis in a SCRM context. Second, we introduce cross-impact analysis as a scenariobased supplier selection methodology. We demonstrate how cross-impact analysis can be used to provide supply chain decision-makers with probability estimates of the future viability of the members of a given set of possible suppliers in a backdrop of macroeconomic risk. The third and final paper in the series incorporates the probability estimates resulting from a cross-impact analysis exercise into a hybrid stochastic mixed-integer programming (SMIP) technique CIA-SMIP. We demonstrate how the CIA-SMIP approach can be utilized as a single-source supplier selection model. In its totality, this dissertation represents a step towards the theoretical framing of the human impact on SCRM into two main distinguishable areas: microeconomic and macroeconomic.
Identifying Product Defects from User Complaints: A Probabilistic Defect Model
Zhang, Xuan; Qiao, Zhilei; Tang, Lijie; Fan, Weiguo Patrick; Fox, Edward A.; Wang, Gang Alan (Department of Computer Science, Virginia Polytechnic Institute & State University, 2016-03-02)
The recent surge in using social media has created a massive amount of unstructured textual complaints about products and services. However, discovering and quantifying potential product defects from large amounts of unstructured text is a nontrivial task. In this paper, we develop a probabilistic defect model (PDM) that identifies the most critical product issues and corresponding product attributes, simultaneously. We facilitate domain-oriented key attributes (e.g., product model, year of production, defective components, symptoms, etc.) of a product to identify and acquire integral information of defect. We conduct comprehensive evaluations including quantitative evaluations and qualitative evaluations to ensure the quality of discovered information. Experimental results demonstrate that our proposed model outperforms existing unsupervised method (K-Means Clustering), and could find more valuable information. Our research has significant managerial implications for mangers, manufacturers, and policy makers.

Browsing by Author "Wang, Gang Alan"

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