Computer Sciences Commons^™

Efficient Column-Oriented Processing For Mutual Subspace Skyline Queries, Tao Jiang, Bin Zhang, Dan Lin, Yunjun Gao, Qing Li

Computer Science Faculty Research & Creative Works

A mutual skyline query will enable some new applications, such as marketing analysis, task allocation, and personalized matching. Algorithms for efficient processing of this query have been recently proposed in the literature. Those approaches use the R-tree indexes and apply a series of pruning criteria toward efficient processing. However, they are characterized by several limitations: (1) they cannot process different interests on attributes for skyline and reverse skyline, (2) they require a multidimensional index, which suffers from performance degradation, especially in high-dimensional space, and (3) they do not support vertically decomposed data that is a natural and intuitive choice for …

Cusz: An Efficient Gpu-Based Error-Bounded Lossy Compression Framework For Scientific Data, Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelityfor postanalysis. Because supercomputers and HPC applicationsare becoming heterogeneous using accelerator-based architectures,in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existingstate-of-the-art GPU-based lossy compressors suffer from eitherlow compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version,cuSZ, for one of the best error-bounded lossy compressors-SZ.To the best of our knowledge, cuSZ is the first error-boundedlossy compressor on GPUs for scientific data. …

An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of …

Big Data Energy Management, Analytics And Visualization For Residential Areas, Ragini Gupta, A. R. Al-Ali, Imran A. Zualkernan, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer's behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor …

Analysis Of Distributed And Autonomous Scheduling Functions For 6tisch Networks, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The 6TiSCH architecture is expected to play a significant role to enable the Internet of Things paradigm also in industrial environments, where reliability and timeliness are of paramount importance to support critical applications. Many research activities have focused on the Scheduling Function (SF) used for managing the allocation of communication resources in order to guarantee the application requirements. Two different approaches have mainly attracted the interest of researchers, namely distributed and autonomous scheduling. Although many different (both distributed and autonomous) SFs have been proposed and analyzed, a direct comparison of these two approaches is still missing. In this work, we …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …

Hierarchical Syntactic Models For Human Activity Recognition Through Mobility Traces, Enrico Casella, Marco Ortolani, Simone Silvestri, Sajal K. Das

Computer Science Faculty Research & Creative Works

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical …

Distributed Adaptive State Estimation And Tracking Scheme For Nonlinear Systems Using Active Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel adaptive neural network (NN) based distributed state estimation scheme for a heterogeneous sensor network (HSN), to estimate the state vector of an unknown nonlinear process/target by using sensed output when the target input remains unknown. The active nodes in the HSN can sense the target output based on the detection range. By using a connected graph, the active nodes will communicate their estimated state vector from their adaptive NN observer to other passive nodes in the neighborhood that cannot sense the target, so that they can estimate the target state vector. Next, a subset of …

Optimal Control Of Linear Continuous-Time Systems In The Presence Of State And Input Delays With Application To A Chemical Reactor, Rohollah Moghadam, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the optimal regulation of linear continuous-time systems with state and input delays is introduced by utilizing a quadratic cost function and state feedback. The Lyapunov-Krakovskii functional incorporating state and input delays is defined as a value function. Next, the Bellman type equation is formulated, and a delay Algebraic Riccati equation (DARE) over infinite time horizon is derived. By using the stationarity condition for the Bellman type equation, the optimal control input is obtained. It is demonstrated that the proposed optimal control input makes the closed-loop system asymptotically stable. Finally, simulation results confirm the theoretical claims by applying …

Dynamic Trajectory Generation And A Robust Controller To Intercept A Moving Ball In A Game Setting, Ravi Prakash, Laxmidhar Behera, Santhakumar Mohan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Complex and interactive robot manipulation skills, such as playing a game of table tennis against a human opponent, are a novel problem with multifaceted challenges. Accurate dynamic trajectory generation in order to respond to the tennis ball from the opponent and a novel control scheme for robust and high-performance tracking of the ball in such dynamic situations is a prerequisite to winning the game. In this paper, the dynamic movement primitives (DMPs) are employed for the stable generation of dynamic trajectories in the presence of environmental uncertainties such as ball position and velocity, opponent position and velocity and so on. …

Availability-Resilient Control Of Uncertain Linear Stochastic Networked Control Systems, Chandreyee Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

The resilient output feedback control of linear networked control (NCS) system with uncertain dynamics in the presence of Gaussian noise is presented under the denial of service (DoS) attacks on communication networks. The DoS attacks on the sensor-to-controller (S-C) and controller-to-actuator (C-A) networks induce random packet losses. The NCS is viewed as a jump linear system, where the linear NCS matrices are a function of induced losses that are considered unknown. A set of novel correlation detectors is introduced to detect packet drops in the network channels using the property of Gaussian noise. By using an augmented system representation, the …

Online Optimal Adaptive Control Of A Class Of Uncertain Nonlinear Discrete-Time Systems, Rohollah Moghadam, Pappa Natarajan, Krishnan Raghavan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a multi-layer neural network (MNN) based online optimal adaptive regulation of a class of nonlinear discrete-time systems in affine form with uncertain internal dynamics is introduced. The multi-layer neural networks (MNN)-based actor-critic framework is utilized to estimate the optimal control input and cost function. The temporal difference (TD) error is derived from the difference between actual and estimated cost function. The MNN weights of both critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference and control policy errors. The proposed approach does not require the selection of any basis …

Motifs Enable Communication Efficiency And Fault-Tolerance In Transcriptional Networks, Satyaki Roy, Preetam Ghosh, Dipak Barua, Sajal K. Das

Computer Science Faculty Research & Creative Works

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B …

Proof-Of-Activity Consensus Protocol Based On A Network's Active Nodes, Roman Belfer, Antonina Kashtalian, Andrii Nicheporuk, George Markowsky, Anatoliy Sachenko

Computer Science Faculty Research & Creative Works

The paper proposes a new socially oriented protocol that avoids pseudo-decentralization and monopolization of the network, increases the availability of the system, provides a fair selection of potential validator nodes, and provides a fair reward for creating new blocks and adding them to the blockchain. Our proposed protocol provides for the creation and addition of new blocks to a blockchain. Defines a node validator, which will create the next block according to its useful activity in the network according to predefined conditions, which can be formed according to the requirements of the system and will satisfy the individual needs of …

Perceived-Value-Driven Optimization Of Energy Consumption In Smart Homes, Atieh R. Khamesi, Simone Silvestri, Denise A. Baker, Alessandra De Paola

Computer Science Faculty Research & Creative Works

Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances …

Methods And Systems For Group-Based Energy Harvesting, Tae-Jin Lee, Kyoung Min Kim, Ce Zhou

Computer Science Faculty Research & Creative Works

Provided is a method and system for group - based energy harvesting. The group - based energy harvesting method per formed by an access point in an energy harvesting system includes allocating an access period in each group including at least one station , receiving an energy state from the station in the group corresponding to the allocated access period , and scheduling stations in each group as a data transmission station or an energy reception station based on the received energy state.

Efficiently Discovering Users Connectivity With Local Information In Online Social Networks, Na Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

People's activities in Online Social Networks (OSNs) have generated a massive volume of data to which tremendous attention has been paid in academia and industry. With such data, researchers and third-parties can analyze human beings’ behaviors in social communities and develop more user-friendly services and applications to meet people's needs. However, often times, they face a big challenge of acquiring the data, as the access to such data is restricted by their collectors (e.g., Facebook and Twitter), due to various reasons, such as their user's privacy. In this paper, we intend to shed light on leveraging limited local social network …

Distributed Adaptive State Estimation And Tracking By Using Active-Passive Sensor Networks, Akhilesh Raj, Sarangapani Jagannathan, Tansel Yucelen

Electrical and Computer Engineering Faculty Research & Creative Works

Heterogeneous sensor networks (HSN) find a wide range of applications in the field of military and civilian environments, where sensor nodes are utilized to estimate the position of a target with both dynamics and control input being unknown for the purposes of tracking. In the HSN, nodes are considered active depending upon their ability to sense the target output while the others are taken passive. Accurate estimation requires local information exchange among the spatially located sensor nodes, so that the active nodes as well as the passive nodes converge simultaneously to the same value. The local information exchange among the …

A Collusion-Resistant Revocable Attribute-Based Encryption Scheme For Secure Data Sharing In Cloud, Azharul Islam, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Attribute-based encryption (ABE) is a prominent cryptographic tool for secure data sharing in the cloud because it can be used to enforce very expressive and fine-grained access control on outsourced data. The revocation in ABE remains a challenging problem as most of the revocation techniques available today, suffer from the collusion attack. The revocable ABE schemes which are collusion resistant require the aid of a semi-trusted manager to achieve revocation. More specifically, the semi-trusted manager needs to update the secret keys of nonrevoked users followed by a revocation. This introduces computation and communication overhead, and also increases the overall security …

Crowdprivacy: Publish More Useful Data With Less Privacy Exposure In Crowdsourced Location-Based Services, Fang Jing Wu, Tie Luo

Computer Science Faculty Research & Creative Works

Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users' mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect-in both spatial and temporal domains-is the "best" for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility …

Values Of Artificial Intelligence In Marketing, Yingrui Xi

Masters Theses

“Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

Off-Policy Q-Learning For Anti-Interference Control Of Multi-Player Systems, Jinna Li, Zhenfei Xiao, Tianyou Chai, Frank L. Lewis, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be known. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning due to the well-known advantages of off-policy Q-learning over on-policy Q-learning. All of the players work hard together for the goal of minimizing their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

Towards Efficacy And Efficiency In Sparse Delay Tolerant Networks, Douglas John Mcgeehan

Doctoral Dissertations

"The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people's movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] …

Attack Detection And Mitigation In Mobile Robot Formations, Arnold Fernandes

Masters Theses

"A formation of cheap and agile robots can be deployed for space, mining, patrolling, search and rescue applications due to reduced system and mission cost, redundancy, improved system accuracy, reconfigurability, and structural flexibility. However, the performance of the formation can be altered by an adversary. Therefore, this thesis investigates the effect of adversarial inputs or attacks on a nonholonomic leader-follower-based robot formation and introduces novel detection and mitigation schemes.

First, an observer is designed for each robot in the formation in order to estimate its state vector and to compute the control law. Based on the healthy operation of the …

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …

On Predicting Stopping Time Of Human Sequential Decision-Making Using Discounted Satisficing Heuristic, Mounica Devaguptapu

Masters Theses

“Human sequential decision-making involves two essential questions: (i) "what to choose next?", and (ii) "when to stop?". Assuming that the human agents choose an alternative according to their preference order, our goal is to model and learn how human agents choose their stopping time while making sequential decisions. In contrary to traditional assumptions in the literature regarding how humans exhibit satisficing behavior on instantaneous utilities, we assume that humans employ a discounted satisficing heuristic to compute their stopping time, i.e., the human agent stops working if the total accumulated utility goes beyond a dynamic threshold that gets discounted with time. …

Computer Vision Based Deep Learning Models For Cyber Physical Systems, Muhammad Monjurul Karim

Masters Theses

“Cyber-Physical Systems (CPSs) are complex systems that integrate physical systems with their counterpart cyber components to form a close loop solution. Due to the ability of deep learning in providing sensor data-based models for analyzing physical systems, it has received increased interest in the CPS community in recent years. However, developing vision data-based deep learning models for CPSs remains critical since the models heavily rely on intensive, tedious efforts of humans to annotate training data. Besides, most of the models have a high tradeoff between quality and computational cost. This research studies deep learning algorithms to achieve affordable and upgradable …

Coordinated Container Migration And Base Station Handover In Mobile Edge Computing, Mao V. Ngo, Tie Luo, Hieu T. Hoang, Q. S. Tony Quek

Computer Science Faculty Research & Creative Works

Offloading computationally intensive tasks from mobile users (MUs) to a virtualized environment such as containers on a nearby edge server, can significantly reduce processing time and hence end-to-end (E2E) delay. However, when users are mobile, such containers need to be migrated to other edge servers located closer to the MUs to keep the E2E delay low. Meanwhile, the mobility of MUs necessitates handover among base stations in order to keep the wireless connections between MUs and base stations uninterrupted. In this paper, we address the joint problem of container migration and base-station handover by proposing a coordinated migration-handover mechanism, with …

Cyber Physical Security Of Avionic Systems, Anusha Thudimilla

Doctoral Dissertations

“Cyber-physical security is a significant concern for critical infrastructures. The exponential growth of cyber-physical systems (CPSs) and the strong inter-dependency between the cyber and physical components introduces integrity issues such as vulnerability to injecting malicious data and projecting fake sensor measurements. Traditional security models partition the CPS from a security perspective into just two domains: high and low. However, this absolute partition is not adequate to address the challenges in the current CPSs as they are composed of multiple overlapping partitions. Information flow properties are one of the significant classes of cyber-physical security methods that model how inputs of a …