Physical Sciences and Mathematics Commons^™

Understanding Sleep In Pediatric Patients With Sickle Cell Disease Admitted For Vaso-Occlusive Pain Crisis Through Objective Data, Kalindi Narine, Fan Yang, Tanvi Banerjee, Jude Jonassaint, Nirmish Shah

Computer Science and Engineering Faculty Publications

Sickle cell disease (SCD) is an inherited red cell disorder that leads to sickling of red blood cells, anemia and vaso-occlusion. The most common reason for hospitalization and morbidity in children is pain due to vaso-occlusive crisis (VOC). Importantly, poor sleep quality can lead to increased pain the subsequent day and nocturnal pain leads to reduced deep sleep, both which can then modify pain sensitivity. Studies using sleep diaries have shown this cyclical relationship between sleep and pain. Frequent occurrences of restless sleep are therefore believed to contribute to an increased severity and intensity of pain episodes. There is very …

Tracking You Through Dns Traffic: Linking User Sessions By Clustering With Dirichlet Mixture Model, Mingxuan Sun, Junjie Zhang, Guangyue Xu, Dae Wook Kim

Computer Science and Engineering Faculty Publications

The Domain Name System (DNS), which does not encrypt domain names such as "bank.us" and "dentalcare.com", commonly accurately reflects the specific network services. Therefore, DNS-based behavioral analysis is extremely attractive for many applications such as forensics investigation and online advertisement. Traditionally, a user can be trivially and uniquely identified by the device’s IP address if it is static (i.e., a desktop or a laptop). As more and more wireless and mobile devices are deeply ingrained in our lives and the dynamic IP address such as DHCP has been widely applied, it becomes almost impossible to use one IP address to …

Teaching Image Processing And Visualization Principles To Medicine Students, Christina Gillmann, Thomas Wischgoll, Jose T. Hernandez, Hans Hagen

Computer Science and Engineering Faculty Publications

Although image processing becomes increasingly important in most applications such as medicine, image processing and visualization is usually not a part of the medical education and therefore not widely spread in clinical daily routine. Contrary to students from computer science, medical students are usually not familiar to computational models or algorithms and require a different view of the algorithms instead of knowing each computational detail. To solve this problem this paper presents the concept of a lecture that aims to impart image processing and visualization principals for students in medicine in order to pioneer a higher acceptance and propagation of …

What Does Explainable Ai Really Mean? A New Conceptualization Of Perspectives, Derek Doran, Sarah Schulz, Tarek R. Besold

Computer Science and Engineering Faculty Publications

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human …

Explaining Trained Neural Networks With Semantic Web Technologies: First Steps, Md Kamruzzaman Sarker, Ning Xie, Derek Doran, Michael L. Raymer, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The ever increasing prevalence of publicly available struc-tured data on the World Wide Web enables new applications in a varietyof domains. In this paper, we provide a conceptual approach that lever-ages such data in order to explain the input-output behavior of trainedartificial neural networks. We apply existing Semantic Web technologiesin order to provide an experimental proof of concept.

Discovering Explanatory Models To Identify Relevant Tweets On Zika, Roopteja Muppalla, Michele Miller, Tanvi Banerjee, William L. Romine

Kno.e.sis Publications

Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the …

A Knowledge Graph Framework For Detecting Traffic Events Using Stationary Cameras, Roopteja Muppalla, Sarasi Lalithsena, Tanvi Banerjee, Amit Sheth

Kno.e.sis Publications

With the rapid increase in urban development, it is critical to utilize dynamic sensor streams for traffic understanding, especially in larger cities where route planning or infrastructure planning is more critical. This creates a strong need to understand traffic patterns using ubiquitous sensors to allow city officials to be better informed when planning urban construction and to provide an understanding of the traffic dynamics in the city. In this study, we propose our framework ITSKG (Imagery-based Traffic Sensing Knowledge Graph) which utilizes the stationary traffic camera information as sensors to understand the traffic patterns. The proposed system extracts image-based features …

Intuitive Error Space Exploration Of Medical Image Data In Clinical Daily Routine, Christina Gillmann, Pablo Arbeláez, José Tiberio Hernández Peñaloza, Hans Hagen, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

Medical image data can be affected by several image errors. These errors can lead to uncertain or wrong diagnosis in clinical daily routine. A large variety of image error metrics are available that target different aspects of image quality forming a highdimensional error space, which cannot be reviewed trivially. To solve this problem, this paper presents a novel error space exploration technique that is suitable for clinical daily routine. Therefore, the clinical workflow for reviewing medical data is extended by error space cluster information, that can be explored by user-defined selections. The presented tool was applied to two real-world datasets …

Eassistant: Cognitive Assistance For Identification And Auto-Triage Of Actionable Conversations, Hamid R. Motahari Nezhad, Kalpa Gunaratna, Juan Cappi

Kno.e.sis Publications

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in …

What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, And Prevention, Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William L. Romine, Amit Sheth

Kno.e.sis Publications

Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus.

Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment.

Methods: A combination of natural language processing and machine learning techniques was used to determine what people were …

Data-Driven Network-Centric Threat Assessment, Dae Wook Kim

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As the Internet has grown increasingly popular as a communication and information sharing platform, it has given rise to two major types of Internet security threats related to two primary entities: end-users and network services. First, information leakages from networks can reveal sensitive information about end-users. Second, end-users systems can be compromised through attacks on network services, such as scanning-and-exploit attacks, spamming, drive-by downloads, and fake anti-virus software. Designing threat assessments to detect these threats is, therefore, of great importance, and a number of the detection systems have been proposed. However, these existing threat assessment systems face significant challenges in …

Deep Learning Approach For Intrusion Detection System (Ids) In The Internet Of Things (Iot) Network Using Gated Recurrent Neural Networks (Gru), Manoj Kumar Putchala

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The Internet of Things (IoT) is a complex paradigm where billions of devices are connected to a network. These connected devices form an intelligent system of systems that share the data without human-to-computer or human-to-human interaction. These systems extract meaningful data that can transform human lives, businesses, and the world in significant ways. However, the reality of IoT is prone to countless cyber-attacks in the extremely hostile environment like the internet. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. To secure an IoT system, the traditional high-end security …

Settings Protection Add-On: A User-Interactive Browser Extension To Prevent The Exploitation Of Preferences, Venkata Naga Siva Seelam

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The abuse of browser preferences is a significant application security issue, despite numerous protections against automated software changing these preferences. Browser hijackers modify user’s desired preferences by injecting malicious software into the browser. Users are not aware of these modifications, and the unwanted changes can annoy the user and circumvent security preferences. Reverting these changes is not easy, and users often have to go through complicated sequences of steps to restore their preferences to the previous values. Tasks to resolve this issue include uninstalling and re-installing the browser, resetting browser preferences, and installing malware removal tools. This thesis describes a …

Panel: Teaching To Increase Diversity And Equity In Stem, Helen H. Hu, Douglas Blank, Albert Chan, Travis E. Doom

Computer Science and Engineering Faculty Publications

TIDES (Teaching to Increase Diversity and Equity in STEM) is a three-year initiative to transform colleges and universities by changing what STEM faculty, especially CS instructors, are doing in the classroom to encourage the success of their students, particularly those that have been traditionally underrepresented in computer science.Each of the twenty projects selected proposed new inter-disciplinary curricula and adopted culturally sensitive pedagogies, with an eye towards departmental and institutional change. The four panelists will each speak about their TIDES projects, which all involved educating faculty about cultural competency. Three of the panelists infused introductory CS courses with applications from other …

Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

Computer Science and Engineering Faculty Publications

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms …

An Industrial Vision System To Analyze The Wear Of Cutting Tools, Christina Gillmann, Tobias Post, Benjamin Kirsch, Thomas Wischgoll, Jörg Hartig, Bernd Hamann, Hans Hagen, Jan C. Aurich

Computer Science and Engineering Faculty Publications

The wear behavior of cutting tools directly affects the quality of the machined part. The measurement and evaluation of wear is a time consuming process and is subjective. Therefore, an image-based wear measurement that can be computed automatically based on given image series of cutting tools and an objective way to review the resulting wear is presented in this paper. The presented method follows the industrial vision system pipeline where images of cutting tools are used as input which are then transformed through suitable image processing methods to prepare them for the computation of a novel image based wear measurement. …

A High-Dimensional Data Quality Metric Using Pareto Optimality, Tobias Post, Thomas Wischgoll, Bernd Hamann, Hans Hagen

Computer Science and Engineering Faculty Publications

The representation of data quality within established high-dimensional data visualization techniques such as scatterplots and parallel coordinates is still an open problem. This work offers a scale-invariant measure based on Pareto optimality that is able to indicate the quality of data points with respect to the Pareto front. In cases where datasets contain noise or parameters that cannot easily be expressed or evaluated mathematically, the presented measure provides a visual encoding of the environment of a Pareto front to enable an enhanced visual inspection.

Identifying Depressive Disorder In The Twitter Population, Goonmeet Bajaj, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth

Kno.e.sis Publications

Depression is a highly prevalent public health challenge and a major cause of disability across the globe.

• Annually 6.7% of Americans (that is, more than 16 million).
• Traditional approaches to curb depression involve survey·based methods via phone or online questionnaires.
• Large temporal gaps and cognitive bias.

Social media provides a method for learning users' feelings, emotions, behaviors, and decisions in real-time.

Virtuo-Its: An Interactive Tutoring System To Teach Virtual Memory Concepts Of An Operating System, Venkata Krishna Kanth Musunuru

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Interactive tutoring systems are software applications that help individuals to learn difficult concepts. They can allow students to interact with ideas from essential mathematics to more complicated subjects like software engineering. This thesis concentrates on one such interactive tutoring system (ITS) designed for teaching concepts related to operating system virtual memory. Operating system concepts can be troublesome to learn without having someone or something to explain them. Even when an instructor is able to provide detailed explanations, it is still exceptionally difficult for students without a computer science foundation to comprehend these concepts. Students require a sophisticated set of mental …

A Novel Approach For Classifying Gene Expression Data Using Topic Modeling, Soon Jye Kho, Himi Yalamanchili, Michael L. Raymer, Amit Sheth

Kno.e.sis Publications

Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA inclustering as well as in classification of cancer and healthy tissues using lung cancer …

Road Accidents Bigdata Mining And Visualization Using Support Vector Machines, Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Kno.e.sis Publications

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new …

Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng

Kno.e.sis Publications

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and …

A Semantics-Based Measure Of Emoji Similarity, Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

Kno.e.sis Publications

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test …

Gait Analysis From Wearable Devices Using Image And Signal Processing, Bradley A. Schneider

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We present the results of analyzing gait motion in-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera and are validated using the ground truth dataset. We perform an additional data collection with both the camera and a body-worn accelerometer …

Characterizing Concepts In Taxonomy For Entity Recommendations, Siva Kumar Cheekula

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Entity recommendation systems are enormously popular on the Web. These systems harness manually crafted taxonomies for improving recommendations. For example, Yahoo created the Open Directory Project for search and recommendation, and Amazon utilizes its own product taxonomy. While these taxonomies are of high quality, it is a labor and time-intensive process to manually create and keep them up to date. Instead, in this era ofWeb 2.0 where users collaboratively create large amounts of information on the Web, it is possible to utilize user-generated content to automatically generate good quality taxonomies. However, harnessing such taxonomies for entity recommendations has not been …

A Power Iteration Based Co-Training Approach To Achieve Convergence For Multi-View Clustering, Pavankalyan Yallamelli

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Collecting diversified opinions is the key to achieve "the Wisdom of Crowd". In this work, we propose to use a novel multi-view clustering method to group the crowd so that diversified opinions can be effectively sampled from different groups of people.Clustering is the process of dividing input data into possible subsets, where every element (entity) in each subset is considered to be related by some similarity measure. For example, a set of social media users can be clustered using their locations or common interests. However, real-world data is often best represented by multiple views/dimensions. For example, a set of social …

Multi-Class Classification Of Textual Data: Detection And Mitigation Of Cheating In Massively Multiplayer Online Role Playing Games, Naga Sai Nikhil Maguluri

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The success of any multiplayer game depends on the player’s experience. Cheating/Hacking undermines the player’s experience and thus the success of that game. Cheaters, who use hacks, bots or trainers are ruining the gaming experience of a player and are making him leave the game. As the video game industry is a constantly increasing multibillion dollar economy, it is crucial to assure and maintain a state of security. Players reflect their gaming experience in one of the following places: multiplayer chat, game reviews, and social media. This thesis is an exploratory study where our goal is to experiment and propose …

Sched-Its: An Interactive Tutoring System To Teach Cpu Scheduling Concepts In An Operating Systems Course, Bharath Kumar Koya

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Operating systems is an essential course in computer science curriculum, which helps students to develop a mental model of how computer operating systems work. The internal mechanisms and processes of an operating system (OS) are often complex, non-deterministic and intangible which makes them difficult for students to understand. One such concept is central processing unit (CPU) scheduling. CPU scheduling forms the basis of the multiprogramming in an OS. In practice, OS courses involve classroom lectures describing high-level abstractions of the concepts, and students complete programming assignments to apply the material in a more concrete way. Depending on the programming assignments, …

Detection Of Ddos Attacks Against The Sdn Controller Using Statistical Approaches, Basheer Husham Ali Al-Mafrachi

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In traditional networks, switches and routers are very expensive, complex, and inflexible because forwarding and handling of packets are in the same device. However, Software Defined Networking (SDN) makes networks design more flexible, cheaper, and programmable because it separates the control plane from the data plane. SDN gives administrators of networks more flexibility to handle the whole network by using one device which is the controller. Unfortunately, SDN faces a lot of security problems that may severely affect the network operations if not properly addressed. Threat vectors may target main components of SDN such as the control plane, the data …

Detecting Information Leakage In Android Malware Using Static Taint Analysis, Soham P. Kelkar

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According to Google, Android now runs on 1.4 billion devices. The growing popularity has attracted attackers to use Android as a platform to conduct malicious activities. To achieve these malicious activities some attacker choose to develop malicious Apps to steal information from the Android users. As the modern day smartphones process, a lot of sensitive information, information security, and privacy becoming a potential target for the attacker. The malicious Apps steal information from the infected phone and send this information to the attacker-controlled URLs using various Android sink functions. Therefore, it necessary to protect data as it can prove detrimental …