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Articles 1 - 30 of 37
Full-Text Articles in Physical Sciences and Mathematics
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview, Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview, Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Computer Science Faculty Research & Creative Works
Deep Convolutional Networks have been very successful in visual recognition tasks recently. Previous works visualize learned features at different layers o help people to understand how CNNs learn visual recognition tasks. However they do not help to accelerate the training process. We use Paraview to provides both qualitative and quantitative visualization that help understand the learning procedure, tune the learning parameters and direct merging and pruning of neural networks.
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview (Poster), Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview (Poster), Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Computer Science Faculty Research & Creative Works
Deep Convolutional Networks have been very successful in visual recognition tasks recently. Lots of previous works aimed to help people to get senses of why those biology-inspired networks achieved such good performances. Deconvnet[1], Guided propagation[2] and a comprehensive visualization tool box[3] can help people to see features learned at different layers of the networks. These works in some extent provided understanding and support for the biology origin of how convolutional networks emulate visual recognition tasks. However, due to the complexity of searching in very high dimensional parameter space, the whole training remains in black-boxes. Normally a large network needs weeks …
Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam
Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam
Engineering Management and Systems Engineering Faculty Research & Creative Works
Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple individuals or study units are observed and measured at tens, hundreds, or thousands of occasions. The spacing of measurements in time for each individual can be regular or irregular, fixed or random, and the number of characteristics measured at each occasion may be few or many. Such data can also arise in situations involving continuous-time measurements of recurrent events. Generalized linear models (GLMs) are usually considered for the analysis of correlated non-normal data, while multivariate analysis of variance (MANOVA) is another …
Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun
Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun
Computer Science Faculty Research & Creative Works
The utilization of onsite generation system with renewable sources in manufacturing plants plays a critical role in improving the resilience, enhancing the sustainability, and bettering the cost effectiveness for manufacturers. When designing the capacity of onsite generation system, the manufacturing energy load needs to be met and the cost for building and operating such onsite system with renewable sources are two critical factors need to be carefully quantified. Due to the randomness of machine failures and the variation of local weather, it is challenging to determine the energy load and onsite generation supply at different time periods. In this paper, …
Augmenting Amdahl's Second Law: A Theoretical Model To Build Cost-Effective Balanced Hpc Infrastructure For Data-Driven Science, Arghya Kusum Das, Jaeki Hong, Sayan Goswami, Richard Platania, Kisung Lee, Wooseok Chang, Seung Jong Park, Ling Liu
Augmenting Amdahl's Second Law: A Theoretical Model To Build Cost-Effective Balanced Hpc Infrastructure For Data-Driven Science, Arghya Kusum Das, Jaeki Hong, Sayan Goswami, Richard Platania, Kisung Lee, Wooseok Chang, Seung Jong Park, Ling Liu
Computer Science Faculty Research & Creative Works
High-performance analysis of big data demands more computing resources, forcing similar growth in computation cost. So, the challenge to the HPC system designers is providing not only high performance but also high performance at lower cost. For high performance yet cost-effective cyberinfrastructure, we propose a new system model augmenting Amdahl's second law for balanced system to optimize price-performance-ratio. We express the optimal balance among CPU-speed, I/O-bandwidth and DRAM-size (i.e., Amdahl's I/O-and memory-number) in terms of application characteristics and hardware cost. Considering Xeon processor and recent hardware prices, we showed that a system needs almost 0.17GBPS I/O-bandwidth and 3GB DRAM per …
Minimal Coflow Routing And Scheduling In Openflow-Based Cloud Storage Area Networks, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Kisung Lee, Seung Jong Park
Minimal Coflow Routing And Scheduling In Openflow-Based Cloud Storage Area Networks, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Kisung Lee, Seung Jong Park
Computer Science Faculty Research & Creative Works
Researches affirm that coflow scheduling/routing substantially shortens the average application inner communication time in data center networks (DCNs). The commonly desirable critical features of existing coflow scheduling/routing framework includes (1) coflow scheduling, (2) coflow routing, and (3) per-flow rate-limiting. However, to provide the 3 features, existing frameworks require customized computing frameworks, customized operating systems, or specific external commercial monitoring frameworks on software-defined networking (SDN) switches. These requirements defer or even prohibit the deployment of coflow scheduling/routing in production DCNs. In this paper, we design a coflow scheduling and routing framework, MinCOF which has minimal requirements on hosts and switches for …
Coflourish: An Sdn-Assisted Coflow Scheduling Framework For Clouds, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Seung Jong Park
Coflourish: An Sdn-Assisted Coflow Scheduling Framework For Clouds, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Seung Jong Park
Computer Science Faculty Research & Creative Works
Existing coflow scheduling frameworks effectively shorten communication time and completion time of cluster applications. However, existing frameworks only consider available bandwidth on hosts and overlook congestion in the network when making scheduling decisions. Through extensive simulations using the realistic workload probability distribution from Facebook, we observe the performance degradation of the state-of-the-art coflow scheduling framework, Varys, in the cloud environment on a shared data center network (DCN) because of the lack of network congestion information. We propose Coflourish, the first coflow scheduling framework that exploits the congestion feedback assistances from the software-defined-networking (SDN)-enabled switches in the networks for available bandwidth …
Defining Consciousness, Adam Bateman
Defining Consciousness, Adam Bateman
Missouri S&T’s Peer to Peer
A researcher trying to develop a conscious artificial intelligence or examine consciousness in plants would be completely unable to do so without first obtaining a clear, concise, and global definition. This idea is what originally inspired my research. The main method of research to be used will be to thoroughly examine scholarly articles pertaining to consciousness and different theories of the mind. After gathering data and different ideas, I will create a definition that is plausible, and is optimized in terms of being useful to researchers. Currently, the issue is that there are an incredible amount of mental features that …
Biosensors For Cancer Detection Applications, Shannon Griffin
Biosensors For Cancer Detection Applications, Shannon Griffin
Missouri S&T’s Peer to Peer
Cancer is one of the most deadly diseases, and current detection options are ineffective. Recently, a large amount of research has been conducted for the development of biosensors able to detect cancer biomarkers. Many biosensors have been created for cancer detecting purposes. I examined literature reviews outlining current biosensing methods. These reviews provided an overview of the sensing techniques that are currently in existence as well as evaluations of their effectiveness. I also read experimental reports that outline the construction of biosensors fabricated in laboratories and the results of their testings. These papers help to showcase the feasibility and effectiveness …
Exploring Potential Flaws And Dangers Involving Machine Learning Technology, David Nicholas Skoff
Exploring Potential Flaws And Dangers Involving Machine Learning Technology, David Nicholas Skoff
Missouri S&T’s Peer to Peer
This paper seeks to explore the ways in which machine learning and AI may influence the world in the future and the potential for the technology to be misused or exploited. In 1959 Arthur Samuel defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed” (Munoz). This paper will also seek to find out if there is merit to the current worry that robots will take over some jobs based in cognitive abilities. In the past, a human was required to perform these jobs, but with the rise of more complex …
Network Security: Internet Protocol Version Six Security, Hannah Reinbolt
Network Security: Internet Protocol Version Six Security, Hannah Reinbolt
Missouri S&T’s Peer to Peer
It is no secret that the pool of public internet addresses available with Internet Protocol version Four (IPv4) is gone. (Morphy, 2011) Thus the migration to the more roomy Internet Protocol version Six (IPv6) has begun. This migration is a complex process including different security procedures and updates that require time and knowledge. This paper will dive into scientific writings, with databases like Scopus and IEEE, about various security risks in the IPv6 protocol such as tunneling practices, router issues and issues with Internet Protocol Security (IPsec). This paper will overview security practices to better clarify common vulnerabilities in IPv6. …
Automated Breast Cancer Diagnosis Using Deep Learning And Region Of Interest Detection (Bc-Droid), Richard Platania, Jian Zhang, Shayan Shams, Kisung Lee, Seungwon Yang, Seung Jong Park
Automated Breast Cancer Diagnosis Using Deep Learning And Region Of Interest Detection (Bc-Droid), Richard Platania, Jian Zhang, Shayan Shams, Kisung Lee, Seungwon Yang, Seung Jong Park
Computer Science Faculty Research & Creative Works
Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, …
Gaslight: A Comprehensive Fuzzing Architecture For Memory Forensics Frameworks, Andrew Case, Arghya Kusum Das, Seung Jong Park, J. (Ram) Ramanujam, Golden G. Richard
Gaslight: A Comprehensive Fuzzing Architecture For Memory Forensics Frameworks, Andrew Case, Arghya Kusum Das, Seung Jong Park, J. (Ram) Ramanujam, Golden G. Richard
Computer Science Faculty Research & Creative Works
Memory forensics is now a standard component of digital forensic investigations and incident response handling, since memory forensic techniques are quite effective in uncovering artifacts that might be missed by traditional storage forensics or live analysis techniques. Because of the crucial role that memory forensics plays in investigations and because of the increasing use of automation of memory forensics techniques, it is imperative that these tools be resilient to memory smear and deliberate tampering. Without robust algorithms, malware may go undetected, frameworks may crash when attempting to process memory samples, and automation of memory forensics techniques is difficult. In this …
Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park
Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park
Computer Science Faculty Research & Creative Works
Recent advances in deep learning have enabled researchers across many disciplines to uncover new insights about large datasets. Deep neural networks have shown applicability to image, time-series, textual, and other data, all of which are available in a plethora of research fields. However, their computational complexity and large memory overhead requires advanced software and hardware technologies to train neural networks in a reasonable amount of time. To make this possible, there has been an influx in development of deep learning software that aim to leverage advanced hardware resources. In order to better understand the performance implications of deep learning frameworks …
Cnn Based 3d Facial Expression Recognition Using Masking And Landmark Features, Huiyuan Yang, Lijun Yin
Cnn Based 3d Facial Expression Recognition Using Masking And Landmark Features, Huiyuan Yang, Lijun Yin
Computer Science Faculty Research & Creative Works
Automatically recognizing facial expression is an important part for human-machine interaction. In this paper, we first review the previous studies on both 2D and 3D facial expression recognition, and then summarize the key research questions to solve in the future. Finally, we propose a 3D facial expression recognition (FER) algorithm using convolutional neural networks (CNNs) and landmark features/masks, which is invariant to pose and illumination variations due to the solely use of 3D geometric facial models without any texture information. The proposed method has been tested on two public 3D facial expression databases: BU-4DFE and BU-3DFE. The results show that …
Homogenization Of Plastic Deformation In Heterogeneous Lamella Structures, Rui Yuan, Irene J. Beyerlein, Caizhi Zhou
Homogenization Of Plastic Deformation In Heterogeneous Lamella Structures, Rui Yuan, Irene J. Beyerlein, Caizhi Zhou
Materials Science and Engineering Faculty Research & Creative Works
It has been shown that unlike its constituent nanocrystalline (NC) phase, a heterogeneous lamella (HL) composite comprising NC and coarse-grain layers exhibits greatly improved ductility. To understand the origin of this enhancement, we present a 3D discrete dislocation, crystal plasticity finite element model to study the development of strains across this microstructure. Here we show that the HL structure homogenizes the plastic strains in the NC layer, weakening the effect of strain concentrations. These findings can provide valuable insight into the effects of material length scales on material instabilities, which is needed to design heterogeneous structures with superior properties.
Energy-Efficient Multi-Core Scheduling For Real-Time Dag Tasks, Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, Haoyi Xiong
Energy-Efficient Multi-Core Scheduling For Real-Time Dag Tasks, Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, Haoyi Xiong
Computer Science Faculty Research & Creative Works
In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify …
A Deep Learning Framework For Automated Vesicle Fusion Detection, Haohan Li, Zhaozheng Yin, Yingke Xu
A Deep Learning Framework For Automated Vesicle Fusion Detection, Haohan Li, Zhaozheng Yin, Yingke Xu
Computer Science Faculty Research & Creative Works
Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared …
Inelastic Rate Coefficients For Collisions Of C₆Hˉ With H₂ And He, Kyle M. Walker, François Lique, Fabien Dumouchel, Richard Dawes
Inelastic Rate Coefficients For Collisions Of C₆Hˉ With H₂ And He, Kyle M. Walker, François Lique, Fabien Dumouchel, Richard Dawes
Chemistry Faculty Research & Creative Works
The recent detection of anions in the interstellar medium has shown that they exist in a variety of astrophysical environments -- circumstellar envelopes, cold dense molecular clouds and star-forming regions. Both radiative and collisional processes contribute to molecular excitation and de-excitation in these regions so that the ‘local thermodynamic equilibrium’ approximation, where collisions cause the gas to behave thermally, is not generally valid. Therefore, along with radiative coefficients, collisional excitation rate coefficients are needed to accurately model the anionic emission from these environments. We focus on the calculation of state-to-state rate coefficients of the C6H- molecule in …
Hadoop-Based Replica Exchange Over Heterogeneous Distributed Cyberinfrastructures, Richard Platania, Shayan Shams, Chui Hui Chiu, Nayong Kim, Joohyun Kim, Seung Jong Park
Hadoop-Based Replica Exchange Over Heterogeneous Distributed Cyberinfrastructures, Richard Platania, Shayan Shams, Chui Hui Chiu, Nayong Kim, Joohyun Kim, Seung Jong Park
Computer Science Faculty Research & Creative Works
We present Hadoop-based replica exchange (HaRE), a Hadoop-based implementation of the replica exchange scheme developed primarily for replica exchange statistical temperature molecular dynamics, an example of a large-scale, advanced sampling molecular dynamics simulation. By using Hadoop as a framework and the MapReduce model for driving replica exchange, an efficient task-level parallelism is introduced to replica exchange statistical temperature molecular dynamics simulations. In order to demonstrate this, we investigate the performance of our application over various distributed cyberinfrastructures (DCI), including several high-performance computing systems, our cyberinfrastructure for reconfigurable optical networks testbed, the global environment for network innovations testbed, and the CloudLab …
Decodable Network Coding In Wireless Network, Junwei Su
Decodable Network Coding In Wireless Network, Junwei Su
Masters Theses
"Network coding is a network layer technique to improve transmission efficiency. Coding packets is especially beneficial in a wireless environment where the demand for radio spectrum is high. However, to fully realize the benefits of network coding two challenging issues that must be addressed are: (1) Guaranteeing separation of coded packets at the destination, and (2) Mitigating the extra coding/decoding delay. If the destination has all the needed packets to decode a coded packet, then separation failure can be averted. If the scheduling algorithm considers the arrival time of coding pairs, then the extra delay can be mitigated. In this …
Multiple Security Domain Model Of A Vehicle In An Automated Vehicle System, Uday Ganesh Kanteti
Multiple Security Domain Model Of A Vehicle In An Automated Vehicle System, Uday Ganesh Kanteti
Masters Theses
"This thesis focuses on the security of automated vehicle platoons. Specifically, it examines the vulnerabilities that occur via disruptions of the information flows among the different types of sensors, the communications network and the control unit in each vehicle of a platoon. Multiple security domain nondeducibility is employed to determine whether the system can detect attacks. The information flows among the various domains provide insights into the vulnerabilities that exist in the system by showing if an attacker’s actions cannot be deduced. If nondeducibility is found to be true, then an attacker can create an undetectable attack. Defeating nondeducibility requires …
Classification Of Basal Cell Carcinoma Using Telangiectatic Vessels And Machine Learning, Hemanth Yadav Aradhyula
Classification Of Basal Cell Carcinoma Using Telangiectatic Vessels And Machine Learning, Hemanth Yadav Aradhyula
Masters Theses
“Basal cell carcinoma (BCC) is one of the most common types of skin cancer in the United States. Early detection of BCC by noninvasive techniques can decrease delay in treatment and save cost. A recent study estimated that 5.4 million cases of non-melanocytic skin cancer (NMSC) occur each year in the US. BCC accounts for 50% of NMSC cases. Telangiectasia, which appears in most BCCs is an important feature for identification of BCC for an automatic diagnostic system. In this thesis, three methods for detection of telangiectasia present in dermoscopy lesion image (DI) were proposed. Detected telangiectasia in DI was …
Uface: Your Universal Password No One Can See, Nicholas Steven Hilbert
Uface: Your Universal Password No One Can See, Nicholas Steven Hilbert
Masters Theses
"With the advantage of not having to memorize long passwords, facial authentication has become a topic of interest among researchers. However, since many users store images containing their face on social networking sites, a new challenge emerges in preventing attackers from impersonating these users by using these online photos. Another problem with most current facial authentication protocols is that they require an unencrypted image of each registered user's face to compare against. Moreover, they might require the user's device to execute computationally expensive multiparty protocols which presents a problem for mobile devices with limited processing power. Finally, these authentication protocols …
Fusion Of Non-Visual And Visual Sensors For Human Tracking, Wenchao Jiang
Fusion Of Non-Visual And Visual Sensors For Human Tracking, Wenchao Jiang
Doctoral Dissertations
"Human tracking is an extensively researched yet still challenging area in the Computer Vision field, with a wide range of applications such as surveillance and healthcare. People may not be successfully tracked with merely the visual information in challenging cases such as long-term occlusion. Thus, we propose to combine information from other sensors with the surveillance cameras to persistently localize and track humans, which is becoming more promising with the pervasiveness of mobile devices such as cellphones, smart watches and smart glasses embedded with all kinds of sensors including accelerometers, gyroscopes, magnetometers, GPS, WiFi modules and so on. In this …
The Viability Of Advantg Deterministic Method For Synthetic Radiography Generation, Andrew Albert Bingham
The Viability Of Advantg Deterministic Method For Synthetic Radiography Generation, Andrew Albert Bingham
Masters Theses
"Time sensitive and high resolution image simulations are needed for synthetic radiography generation. The standard stochastic approach requires lengthy run times with poor statistics at higher resolutions. The investigation of the viability of a deterministic approach to synthetic radiography image generation was explored. The aim was to analyze a computational time decrease over the stochastic method. ADVANTG was compared to MCNP in multiple scenarios including a Benchtop CT prototype, to simulate high resolution radiography images. By using ADVANTG deterministic code to simulate radiography images the computational time was found to decrease over 10 times compared to the MCNP stochastic approach"--Abstract, …
Multiple Security Domain Nondeducibility Air Traffic Surveillance Systems, Anusha Thudimilla
Multiple Security Domain Nondeducibility Air Traffic Surveillance Systems, Anusha Thudimilla
Masters Theses
"Traditional security models partition the security universe into two distinct and completely separate worlds: high and low level. However, this partition is absolute and complete. The partition of security domains into high and low is too simplistic for more complex cyber-physical systems (CPS). Absolute divisions are conceptually clean, but they do not reflect the real world. Security partitions often overlap, frequently provide for the high level to have complete access to the low level, and are more complex than an impervious wall. The traditional models that handle situations where the security domains are complex or the threat space is ill …
A Bounded Actor-Critic Algorithm For Reinforcement Learning, Ryan Jacob Lawhead
A Bounded Actor-Critic Algorithm For Reinforcement Learning, Ryan Jacob Lawhead
Masters Theses
"This thesis presents a new actor-critic algorithm from the domain of reinforcement learning to solve Markov and semi-Markov decision processes (or problems) in the field of airline revenue management (ARM). The ARM problem is one of control optimization in which a decision-maker must accept or reject a customer based on a requested fare. This thesis focuses on the so-called single-leg version of the ARM problem, which can be cast as a semi-Markov decision process (SMDP). Large-scale Markov decision processes (MDPs) and SMDPs suffer from the curses of dimensionality and modeling, making it difficult to create the transition probability matrices (TPMs) …
Data Analytics Methods For Attack Detection And Localization In Wireless Networks, Yi Ling
Data Analytics Methods For Attack Detection And Localization In Wireless Networks, Yi Ling
Doctoral Dissertations
"Wireless ad hoc network operates without any fixed infrastructure and centralized administration. It is a group of wirelessly connected nodes having the capability to work as host and router. Due to its features of open communication medium, dynamic changing topology, and cooperative algorithm, security is the primary concern when designing wireless networks. Compared to the traditional wired network, a clean division of layers may be sacrificed for performance in wireless ad hoc networks. As a result, they are vulnerable to various types of attacks at different layers of the protocol stack. In this paper, I present real-time series data analysis …
Personalizing Education With Algorithmic Course Selection, Tyler Morrow
Personalizing Education With Algorithmic Course Selection, Tyler Morrow
Masters Theses
"The work presented in this thesis utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, topics or modules) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The original research contribution of this thesis is an algorithm that can generate a schedule of courses with consideration of a student's profile, minimization of cost, and complete adherence to institution requirements. The research problem at hand - a constrained optimization problem with potentially conflicting objectives - is solved by first identifying a minimal sets of courses a student …