Computer Sciences Commons^™

Free Water In T2 Flair Whitematter Hyperintensity Lesions, Xiaowei Yu, Norman Scheel, Lu Zhang, David C. Zhu, Rong Zhang, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Background: White matter (WM) free water (FW) is likely associated with cerebral small vessel disease (CSVD). FWis the fraction of unconstrained water within an image voxel, which can be estimated from diffusion-weighted images. T2-weighted Fluid- Attenuated Inversion Recovery (FLAIR) whitematter hyperintensity (WMH)is awidely used index to assess the damages caused by CSVD. It is critical to characterize howFW content is altered inWMHlesions. In this work, we proposed a data processing framework to assess FW distributions in WMH and normal-appearingWM as well as in differentWMfiber tracts. Method: Single-shell diffusion-weighted image (SS-DWI) and T2 FLAIR image data of 133 cognitively normal (CN) …

Connectionless Edge-Cache Servers For Reducing Cellular Bandwidth Usage In Vehicular Networks, Rui Wang, Jayanthi Rao, Ce Zhou, Subir Biswas

Computer Science Faculty Research & Creative Works

This paper presents a novel caching mechanism based on Connectionless Edge Cache Servers in vehicular networks. The goal is to intelligently cache content within the vehicles and the edge servers so that majority of the vehiclerequested content can be obtained from those caches, thus minimizing the amount of cellular network usage needed for fetching content from a central server. A notable feature of the cache servers in this work is that they do not have backhaul connectivity. This makes the connectionless servers to be relatively less expensive compared to the usual Roadside Service Units (RSUs), and potentially moveable in response …

Lattice-Based Technique To Visualize And Compare Regional Terrorism Using The Global Terrorism Database, Linda Markowsky, George Markowsky

Computer Science Faculty Research & Creative Works

Order-theoretic lattices and their visualizations are proposed as a means of exploring and analyzing databases. The max-complete lattice is formed and the significance of lattice nodes and of the top node are demonstrated. These novel visualizations serve as a useful complement to well-known charting techniques such as bar charts and may extend the knowledge gained from critical databases. The Carver2 dataset is used as an illustrative example, highlighting the formation of the max-complete lattice from the original dataset and the computational advantage provided by compressing the lattice using only its irreducible elements. Regional information from the Global Terrorism Database (GTD), …

Exploiting Semantic Embedding And Visual Feature For Facial Action Unit Detection, Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu

Computer Science Faculty Research & Creative Works

Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter of fact, AU semantic descriptions provide much more information than the binary AU labels alone, thus we propose to exploit the Semantic Embedding and Visual feature (SEV-Net) for AU detection. More specifically, AU semantic embeddings are obtained through both Intra-AU and Inter-AU attention modules, where the Intra-AU attention module …

Preface, Zhe Liu, Fan Wu, Sajal K. Das

Computer Science Faculty Research & Creative Works

No abstract provided.

Optimizing Error-Bounded Lossy Compression For Scientific Data On Gpus, Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

Computer Science Faculty Research & Creative Works

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in …

Secure Data Sharing In Cloud And Iot By Leveraging Attribute-Based Encryption And Blockchain, Md Azharul Islam

Doctoral Dissertations

“Data sharing is very important to enable different types of cloud and IoT-based services. For example, organizations migrate their data to the cloud and share it with employees and customers in order to enjoy better fault-tolerance, high-availability, and scalability offered by the cloud. Wearable devices such as smart watch share user’s activity, location, and health data (e.g., heart rate, ECG) with the service provider for smart analytic. However, data can be sensitive, and the cloud and IoT service providers cannot be fully trusted with maintaining the security, privacy, and confidentiality of the data. Hence, new schemes and protocols are required …

Swill-Tac: Skill-Oriented Dynamic Task Allocation With Willingness For Complex Job In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Allocating tasks to the best-fit candidates is a classical problem in crowdsourcing (CS). Most of the existing approaches assume that the task and candidate knowledge is known in advance and ignore the effect of enrolled candidates' willingness on the CS system's selection decision. For instance, an unwilling candidate assigned to a task may quit without completing it, thus depreciating the utility of the CS platform. In practice, a task or candidate may arrive or leave the CS system dynamically. Moreover, a complex task may be broken into smaller sub-tasks, each requiring a variety of computations and expertise. To overcome these …

Nodesense2vec: Spatiotemporal Context-Aware Network Embedding For Heterogeneous Urban Mobility Data, Dakshak Keerthi Chandra, Jennifer Leopold, Yanjie Fu

Computer Science Faculty Research & Creative Works

The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In this paper, we propose a spatiotemporal context-aware network embedding framework that jointly captures the spatial regularities between objects and the sequential transition patterns of human mobility. First, we model the heterogeneous urban mobility data collected …

Visualization As A Service For Scientific Data, David Pugmire, James Kress, Jieyang Chen, Hank Childs, Jong Choi, Dmitry Ganyushin, Berk Geveci, Mark Kim, Scott Klasky, Xin Liang, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

One of the primary challenges facing scientists is extracting understanding from the large amounts of data produced by simulations, experiments, and observational facilities. The use of data across the entire lifetime ranging from real-time to post-hoc analysis is complex and varied, typically requiring a collaborative effort across multiple teams of scientists. Over time, three sets of tools have emerged: One set for analysis, another for visualization, and a final set for orchestrating the tasks. This trifurcated tool set often results in the manual assembly of analysis and visualization workflows, which are one-off solutions that are often fragile and difficult to …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Robustness Against Attacks And Uncertainties In Smart Cyber-Physical Systems, Prithwiraj Roy

Doctoral Dissertations

Cyber-Physical Systems (CPS) are sensing, processing, and communicating platforms, embedded with physical devices that provide real-time monitoring and control. Security challenges in CPS necessitate solutions that are robust against attacks and uncertainties and provide a seamless operation, especially when used in real-time applications to monitor and secure critical infrastructures. CPS mainly consists of a physical component for sensing or monitoring and a cyber component for processing and communicating. The quality of interactions between physical and cyber systems has direct impacts on the system’s performance and reliability.

CPS plays a major role in smart services and applications within a smart living …

Biochemical Assay Invariant Attestation For The Security Of Cyber-Physical Digital Microfluidic Biochips, Fredrick Eugene Love Ii

Masters Theses

“Due to the devastating global impact that infectious diseases have had, especially in developing countries, the demand for access to adequate resources to combat sickness continues to be a heavy burden. Reliable and affordable diagnostics is a vital first line of defense in fighting outbreaks and providing accurate treatment. Digital microfluidics biochips capable of running multiple diagnostic tests on a single platform are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. Although these systems offer many benefits, processing errors are inherent. Therefore, cyber-physical digital biochips are being investigated that …

Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian

Masters Theses

“Automation with artificial intelligence technology is an emerging field and is widely used in various industries. With the increasing autonomy, learning, and adaptability of intelligent machines such as self-driving cars, it is difficult to regard them as simple tools in human hands. At the same time, a series of problems and challenges such as predictability, interpretability, and causality arise. Trust in self-driving technology will impact the adoption and utilization of autonomous driving technology. A qualitative research methodology, Value-Focused Thinking, is used to identify the values of trust in autonomous driving vehicles and analyze the relationship between these values”--Abstract, page iii.

Detection Of Gaussian Attacks In Power Systems Under A Scalable Kalman Consensus Filter Framework, Arnold Fernandes, Rui Bo, Jonathan W. Kimball, Bruce M. Mcmillin

Electrical and Computer Engineering Faculty Research & Creative Works

The dynamic non-linear state-space model of a power-system consisting of synchronous generators, buses, and static loads has been linearized and a linear measurement function has been considered. A distributed dynamic framework for estimating the state vector of the power system has been designed here. This framework employs a type of distributed Kalman filter (DKF) known as a Kalman consensus filter (KCF) which is located at distributed control centers (DCCs) that fuse locally available noise ridden measurements, state vector estimates of neighboring control centers, and a prediction obtained by the linearized model to obtain a filtered state vector estimate. Further, the …

Computational Intelligent Impact Force Modeling And Monitoring In Hislo Conditions For Maximizing Surface Mining Efficiency, Safety, And Health, Danish Ali

Doctoral Dissertations

"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem.

This research study developed a rigorous mathematical model and a 3D virtual simulation model to …

Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri

Doctoral Dissertations

"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …

Multimodal Neuroscience Data Modeling And Inference, Sima Azizi

Doctoral Dissertations

“Mathematical models can be combined with deep learning and machine learning methods to provide new insights in neuroscience. The field of neuroscience is characterized by rich datasets that include fluid biomarkers, EEG signals, and advanced neuroimages. Recent advances in natural language processing have led to the opportunity to gain additional insights from rapidly growing text databases as well as electronic health records. In this research, we focus on applying computational intelligence methods to the analysis of three different complex data sources: blood levels of disease biomarkers, EEG signals from schizophrenic patients, and disease phenotypes encoded in electronic health records. First, …

Security Against Data Falsification Attacks In Smart City Applications, Venkata Praveen Kumar Madhavarapu

Doctoral Dissertations

Smart city applications like smart grid, smart transportation, healthcare deal with very important data collected from IoT devices. False reporting of data consumption from device failures or by organized adversaries may have drastic consequences on the quality of operations. To deal with this, we propose a coarse grained and a fine grained anomaly based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to detect different attacks. We also built a trust scoring metric to filter out the malicious devices. Another challenging problem is injection of …

Data And Resource Management In Wireless Networks Via Data Compression, Gps-Free Dissemination, And Learning, Xiaofei Cao

Doctoral Dissertations

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting …

Finding All ∈-Good Arms In Stochastic Bandits, Blake Mason, Lalit Jain, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

The pure-exploration problem in stochastic multi-armed bandits aims to find one or more arms with the largest (or near largest) means. Examples include finding an ∈-good arm, best-arm identification, top-k arm identification, and finding all arms with means above a specified threshold. However, the problem of finding all ∈-good arms has been overlooked in past work, although arguably this may be the most natural objective in many applications. For example, a virologist may conduct preliminary laboratory experiments on a large candidate set of treatments and move all ∈-good treatments into more expensive clinical trials. Since the ultimate clinical efficacy is …

Distributed De Novo Assembler For Large-Scale Long-Read Datasets, Sayan Goswami, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Third-generation DNA sequencing technologies such as single-molecule real-time sequencing (SMRT) and nanopore sequencing have the potential to fill the gaps in the existing genome databases since the raw sequences produced by these machines are much longer than those of previous generations and therefore result in more contiguous assemblies. However, these long reads have a high error rate, which makes the assembly process computationally challenging. Moreover, since existing long-read assemblers are designed to run on a single machine, they either take days to complete or run out of memory on even moderate-sized datasets. In this paper, we present a distributed long-read …

Message From The General Chairs, Falko Dressler, Sajal K. Das

Computer Science Faculty Research & Creative Works

No abstract provided.

Special Issue On 6g Wireless Systems, Periklis Chatzimisios, David Soldani, Abbas Jamalipour, Antonio Manzalini, Sajal K. Das

Computer Science Faculty Research & Creative Works

No abstract provided.

Set Operation Aided Network For Action Units Detection, Huiyuan Yang, Taoyue Wang, Lijun Yin

Computer Science Faculty Research & Creative Works

As a large number of parameters exist in deep model-based methods, training such models usually requires many fully AU-annotated facial images. This is true with regard to the number of frames in two widely used datasets: BP4D [31] and DISFA [18], while those frames were captured from a small number of subjects (41, 27 respectively). This is problematic, as subjects produce highly consistent facial muscle movements, adding more frames per subject would only adds more close points in the feature space, and thus the classifier does not benefit from those extra frames. Data augmentation methods can be applied to alleviate …

An Eeg-Based Multi-Modal Emotion Database With Both Posed And Authentic Facial Actions For Emotion Analysis, Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, Weiying Dai, Lijun Yin

Computer Science Faculty Research & Creative Works

Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes. One behavioral change is facial expression, which has been studied extensively over the past few decades. Facial behavior varies with a person's emotion according to differences in terms of culture, personality, age, context, and environment. In recent years, physiological activities have been used to study emotional responses. A typical signal is the electroencephalogram (EEG), which measures brain activity. Most of existing EEG-based emotion analysis has overlooked the role of facial expression changes. There exits little research on the relationship between …

Contextual-Bandit Anomaly Detection For Iot Data In Distributed Hierarchical Edge Computing, Mao V. Ngo, Tie Luo, Hakima Chaouchi, Tony Q.S. Quek

Computer Science Faculty Research & Creative Works

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one …

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …

Adaptive Multimodal Fusion For Facial Action Units Recognition, Huiyuan Yang, Taoyue Wang, Lijun Yin

Computer Science Faculty Research & Creative Works

Multimodal facial action units (AU) recognition aims to build models that are capable of processing, correlating, and integrating information from multiple modalities (i.e., 2D images from a visual sensor, 3D geometry from 3D imaging, and thermal images from an infrared sensor). Although the multimodal data can provide rich information, there are two challenges that have to be addressed when learning from multimodal data: 1) the model must capture the complex cross-modal interactions in order to utilize the additional and mutual information effectively; 2) the model must be robust enough in the circumstance of unexpected data corruptions during testing, in case …

An Evaluation Of The 6tisch Distributed Resource Management Mode, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to ensure reliable and timely communication. 6TiSCH relies on the IEEE TSCH MAC protocol and defines different scheduling approaches for managing TSCH cells, including a distributed (neighbor-to-neighbor) scheduling scheme, where cells are allocated by nodes in a cooperative way. Each node leverages a Scheduling Function (SF) to compute the required number of cells, and the 6top (6P) protocol to negotiate them with neighbors. Currently, the Minimal Scheduling Function (MSF) is under consideration for standardization. However, multiple SFs are expected to be used in real deployments, in …