OS and Networks Commons^™

Robust Decision Making For Stochastic Network Design, Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon

Research Collection School Of Computing and Information Systems

We address the problem of robust decision making for stochastic network design. Our work is motivated by spatial conservation planning where the goal is to take management decisions within a fixed budget to maximize the expected spread of a population of species over a network of land parcels. Most previous work for this problem assumes that accurate estimates of different network parameters (edge activation probabilities, habitat suitability scores) are available, which is an unrealistic assumption. To address this shortcoming, we assume that network parameters are only partially known, specified via interval bounds. We then develop a decision making approach that …

Nlu Framework For Voice Enabling Non-Native Applications On Smart Devices, Soujanya Lanka, Deepika Panthania, Pooja Kushalappa, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Voice is a critical user interface on smart devices (wearables, phones, speakers, televisions) to access applications (or services) available on them. Unfortunately, only a few native applications (provided by the OS developer) are typically voice enabled in devices of today. Since, the utility of a smart device is determined more by the strength of external applications developed for the device, voice enabling non-native applications in a scalable, seamless manner within the device is a critical use case and is the focus of our work. We have developed a Natural Language Understanding (NLU) framework that uses templates supported by the application …

802.11ac Wireless Standard, Brent Marshall

A with Honors Projects

In this student paper, the author compares wireless standard 802.11ac to 802.11n standard. In 2011 the IEEE began development of the newest wireless 802.11 standard. This standard would be known as 802.11ac. Development of this standard continued until 2013 and was approved in January 2014. The new standard was touted as having many improvements over the 802.11n standard. Improvements such as higher data rates, more capacity, being ideal for multimedia, and being more robust.

A Study Of Social Web Data On Buprenorphine Abuse Using Semantic Web Technology, Raminta Daniulaityte, Amit P. Sheth

Kno.e.sis Publications

The Specific Aims of this application are to use a paradigmatic approach that combines Semantic Web technology, Natural Language Processing and Machine Learning techniques to:

1) Describe drug users’ knowledge, attitudes, and behaviors related to the non-medical use of Suboxone and Subutex as discussed on Web-based forums.
2) Identify and describe temporal patterns of non-medical use of Suboxone and Subutex as discussed on Web-based forums.

The research was carried out by an interdisciplinary team of members of the Center for Interventions, Treatment and Addictions Research (CITAR) and the Ohio Center of Excellence in Knowledge- enabled Computing (Kno.e.sis) at Wright State …

Building The Web Of Knowledge With Smart Iot Applications, Amelie Gyrard, Pankesh Patel, Amit P. Sheth, Martin Serrano

Kno.e.sis Publications

The Internet of Things (IoT) is experiencing fast adoption because of its positive impact to change all aspects of our lives, from agriculture in rural areas, to health and wellness, to smart home and smart-x applications in cities. The development of IoT applications and deployment of smart IoT-based solutions is just starting; smart IoT applications will modify our physical world and our interaction with cyber spaces, from how we remotely control appliances at home to how we care for patients or elderly persons. The massive deployment of IoT devices represents a tremendous economic impact and at the same time offers …

Co-Evolution Of Rdf Datasets, Sidra Faisal, Kemele M. Endris, Saeedeh Shekarpour, Sören Auer, Maria-Esther Vidal

Kno.e.sis Publications

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, …

Secure And Authenticated Message Dissemination In Vehicular Ad Hoc Networks And An Incentive-Based Architecture For Vehicular Cloud, Kiho Lim

Theses and Dissertations--Computer Science

Vehicular ad hoc Networks (VANETs) allow vehicles to form a self-organized network. VANETs are likely to be widely deployed in the future, given the interest shown by industry in self-driving cars and satisfying their customers various interests. Problems related to Mobile ad hoc Networks (MANETs) such as routing, security, etc.have been extensively studied. Even though VANETs are special type of MANETs, solutions proposed for MANETs cannot be directly applied to VANETs because all problems related to MANETs have been studied for small networks. Moreover, in MANETs, nodes can move randomly. On the other hand, movement of nodes in VANETs are …

An Indicator Of Inclusion With Applications To Computer Vision, Florentin Smarandache, Ovidiu Ilie Sandru

Branch Mathematics and Statistics Faculty and Staff Publications

In this paper we present an algorithmic process of necessary operations for the automatic movement of a predefined object from a video image in the target region of that image, intended to facilitate the implementation of specialized software applications in solving this kind of problems.

On The Limits And Practice Of Automatically Designing Self-Stabilization, Alex Klinkhamer

Dissertations, Master's Theses and Master's Reports

A protocol is said to be self-stabilizing when the distributed system executing it is guaranteed to recover from any fault that does not cause permanent damage. Designing such protocols is hard since they must recover from all possible states, therefore we investigate how feasible it is to synthesize them automatically. We show that synthesizing stabilization on a fixed topology is NP-complete in the number of system states. When a solution is found, we further show that verifying its correctness on a general topology (with any number of processes) is undecidable, even for very simple unidirectional rings. Despite these negative results, …

Maia And Mandos: Tools For Integrity Protection On Arbitrary Files, Paul J. Bonamy

Dissertations, Master's Theses and Master's Reports

We present the results of our dissertation research, which focuses on practical means of protecting system data integrity. In particular, we present Maia, a language for describing integrity constraints on arbitrary file types, and Mandos, a Linux Security Module which uses verify-on-close to enforce mandatory integrity guarantees. We also provide details of a Maia-based verifier generator, demonstrate that Maia and Mandos introduce minimal delay in performing their tasks, and include a selection of sample Maia specifications.

Controlled Access To Cloud Resources For Mitigating Economic Denial Of Sustainability (Edos) Attacks, Zubair A. Baig, Sadiq M. Sait, Farid Binbeshr

Research outputs 2014 to 2021

Cloud computing is a paradigm that provides scalable IT resources as a service over the Internet. Vulnerabilities in the cloud infrastructure have been readily exploited by the adversary class. Therefore, providing the desired level of assurance to all stakeholders through safeguarding data (sensitive or otherwise) which is stored in the cloud, is of utmost importance. In addition, protecting the cloud from adversarial attacks of diverse types and intents, cannot be understated. Economic Denial of Sustainability (EDoS) attack is considered as one of the concerns that has stalled many organizations from migrating their operations and/or data to the cloud. This is …

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 …