Computer Sciences Commons^™

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 …

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 …

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 …

A Hybrid And Scalable Error Correction Algorithm For Indel And Substitution Errors Of Long Reads, Arghya Kusum Das, Sayan Goswami, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Background: Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods: In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the …

Learning Nearest Neighbor Graphs From Noisy Distance Samples, Blake Mason, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and …

Maxgap Bandit: Adaptive Algorithms For Approximate Ranking, Sumeet Katariya, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or threshold. Estimating an arm's gap requires …

Sample Transfer Optimization With Adaptive Deep Neural Network, Hemanta Sapkota, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error …

Introduction Of A Hybrid Monitor For Cyber-Physical Systems, J. Ceasar Aguma, Bruce M. Mcmillin, Amelia Regan

Computer Science Faculty Research & Creative Works

Computing systems and mobile technologies have changed dramatically since the introduction of firewall technology in 1988. The internet has grown from a simple network of networks to a cyber and physical entity that encompasses the entire planet. Cyber-physical systems(CPS) now control most of the day to day operations of human civilization from autonomous cars to nuclear energy plants. While phenomenal, this growth has created new security threats. These are threats that cannot be blocked by a firewall for they are not only cyber but cyber-physical. In light of these cyber-physical threats, this paper proposes a security measure that promises to …

Social And Geographical Disparities In Twitter Use During Hurricane Harvey, Lei Zou, Nina S.N. Lam, Shayan Shams, Heng Cai, Michelle A. Meyer, Seungwon Yang, Kisung Lee, Seung Jong Park, Margaret A. Reams

Computer Science Faculty Research & Creative Works

Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. However, uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience. This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana. We seek to answer a fundamental question: did social-geographical disparities of Twitter use exist during the three phases of emergency management (preparedness, response, recovery)? We employed a Twitter data mining framework to process the data and calculate two indexes: Ratio and …

Blended Root Finding Algorithm Outperforms Bisection And Regula Falsi Algorithms, Chaman Sabharwal

Computer Science Faculty Research & Creative Works

Finding the roots of an equation is a fundamental problem in various fields, including numerical computing, social and physical sciences. Numerical techniques are used when an analytic solution is not available. There is not a single algorithm that works best for every function. We designed and implemented a new algorithm that is a dynamic blend of the bisection and regula falsi algorithms. The implementation results validate that the new algorithm outperforms both bisection and regula falsi algorithms. It is also observed that the new algorithm outperforms the secant algorithm and the Newton-Raphson algorithm because the new algorithm requires fewer computational …

Use Cases Of Lossy Compression For Floating-Point Data In Scientific Data Sets, Franck Cappello, Sheng Di, Sihuan Li, Xin Liang, Ali Murat Gok, Dingwen Tao, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to …

Collective Representation Learning On Spatiotemporal Heterogeneous Information Networks, Dakshak Keerthi Chandra, Pengyang Wang, Jennifer Leopold, Yanjie Fu

Computer Science Faculty Research & Creative Works

Representation learning is a technique that is used to capture the underlying latent features of complex data. Representation learning on networks has been widely implemented for learning network structure and embedding it in a low dimensional vector space. In recent years, network embedding using representation learning has attracted increasing attention, and many deep architectures have been widely proposed. However, existing network embedding techniques ignore the multi-class spatial and temporal relationships that crucially reflect the complex nature among vertices and links in spatiotemporal heterogeneous information networks(SHINs).

To address this problem, in this paper, we present two types of collective representation learning …

A Deep Learning Approach For Tweet Classification And Rescue Scheduling For Effective Disaster Management, Md. Yasin Kabir, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Every activity in disaster management demands accurate and up-todate information to allow a quick, easy, and cost-efective response to reduce the possible loss of lives and properties. It is a challenging and complex task to acquire information from diferent regions of a disaster-afected area in a timely fashion. The extensive spread and reach of social media and networks such as Twitter allow people to share information in real-time. However, gathering of valuable information requires a series of operations such as (1) processing each tweet for the text classiication, (2) possible location determination of people needing help based on tweets, and …

The 2nd 3d Face Alignment In The Wild Challenge (3dfaw-Video): Dense Reconstruction From Video, Rohith Krishnan Pillai, Laszlo Attila Jeni, Huiyuan Yang, Zheng Zhang, Lijun Yin, Jeffrey F. Cohn

Computer Science Faculty Research & Creative Works

3D face alignment approaches have strong advantages over 2D with respect to representational power and robustness to illumination and pose. Over the past few years, a number of research groups have made rapid advances in dense 3D alignment from 2D video and obtained impressive results. How these various methods compare is relatively unknown. Previous benchmarks addressed sparse 3D alignment and single image 3D reconstruction. No commonly accepted evaluation protocol exists for dense 3D face reconstruction from video with which to compare them. The 2nd 3D Face Alignment in the Wild from Videos (3DFAW-Video) Challenge extends the previous 3DFAW 2016 competition …

Tracking The 6-Dof Flight Trajectory Of Windborne Debris Using Stereophotogrammetry, Chaman Sabharwal, Yanlin Guo

Computer Science Faculty Research & Creative Works

Numerous post-windstorm investigations have reported that windborne debris can cause costly damage to the envelope of buildings in urban areas under strong winds (e.g., during hurricanes or tornados). Thus, understanding the physics of debris flight is of critical importance. Previously developed numerical models describing debris flight physics have not been validated for the complex urban flow environment; such a validation requires experimentally measuring the debris flight trajectory in wind tunnel tests. In this context, this paper proposes a debris measurement algorithm using stereophotogrammetry. This algorithm aims to determine the six-degree-of-freedom (6-DOF) trajectory and velocity of flying debris, addressing the research …

Why Does This Entity Matter? Support Passage Retrieval For Entity Retrieval, Shubham Chatterjee, Laura Dietz

Computer Science Faculty Research & Creative Works

Our goal is to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need. While related to the problem of support passage retrieval, in this paper, we explore two underutilized indicators of relevance: contextual entities and entity salience. The effectiveness of the indicators is studied within a supervised learning-to-rank framework on a dataset from TREC Complex Answer Retrieval. We find that salience is a useful indicator, but it is often not applicable. In contrast, although performance improvements are obtained by using contextual entities, using contextual words still outperforms contextual entities.

Edgesense: Edge-Mediated Spatial-Temporal Crowdsensing, Sijia Yang, Jiang Bian, Licheng Wang, Haojin Zhu, Yanjie Fu, Haoyi Xiong

Computer Science Faculty Research & Creative Works

Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called Edge-Mediated Spatial-Temporal Crowdsensing. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, EdgeSense works on top of a secured peer-To-peer network consisted of participants and propose a novel Decentralized Spatial-Temporal Crowdsensing framework based on Parallelized Stochastic Gradient Descent. To approximate the …

Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin

Computer Science Faculty Research & Creative Works

Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers' behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize …

A Region-Based Deep Learning Algorithm For Detecting And Tracking Objects In Manufacturing Plants, Muhammad Monjurul Karim, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin

Computer Science Faculty Research & Creative Works

In today's competitive production era, the ability to identify and track important objects in a near real-time manner is greatly desired among manufacturers who are moving towards the streamline production. Manually keeping track of every object in a complex manufacturing plant is infeasible; therefore, an automatic system of that functionality is greatly in need. This study was motivated to develop a Mask Region-based Convolutional Neural Network (Mask RCNN) model to semantically segment objects and important zones in manufacturing plants. The Mask RCNN was trained through transfer learning that used a neural network (NN) pre-trained with the MS-COCO dataset as the …

Automated Design Of Random Dynamic Graph Models, Aaron Scott Pope, Daniel R. Tauritz, Chris Rawlings

Computer Science Faculty Research & Creative Works

Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. Examples include modeling the evolution of relationships and communities in a social network or tracking the activity of users within an enterprise computer network. In the case of static graph representations, random graph models are often useful for analyzing and predicting the characteristics of a given network. Even though random dynamic graph models are a trending research topic, the field is still relatively unexplored. The selection of available models is limited and manually developing a model for a new application can be difficult …

Message From Program Chairs, Bruce M. Mcmillin, David Lo

Computer Science Faculty Research & Creative Works

No abstract provided.

Data-Driven Privacy-Preserving Communication, Ye Wang, Prakash Ishwar, Ardhendu S. Tripathy

Computer Science Faculty Research & Creative Works

A communication system including a receiver to receive training data. An input interface to receive input data coupled to a hardware processor and a memory. The hardware processor is configured to initialize the privacy module using the training data. Generate a trained privacy module, by iteratively optimizing an objective function. Wherein for each iteration the objective function is computed by a combination of a distortion of the useful attributes in the transformed data and of a mutual information between the sensitive attributes and the transformed data. Such that the mutual information is estimated by the auxiliary module that maximizes a …

Deepsz: A Novel Framework To Compress Deep Neural Networks By Using Error-Bounded Lossy Compression, Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello

Computer Science Faculty Research & Creative Works

Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources (such as memory, storage, and I/O), significantly restricting their utilization on resource-constrained platforms. Although some DNN simplification methods (such as weight quantization) have been proposed to address this issue, they suffer from either low compression ratios or high compression errors, which may introduce an expensive fine-tuning overhead (i.e., a costly retraining process for the target inference accuracy). In this paper, we propose DeepSZ: …

An Experimental Evaluation Of The 6top Protocol For Industrial Iot Applications, 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 provide reliable and timely communication. 6TiSCH includes a distributed management mode, in which network resources are computed autonomously by nodes and allocated in a cooperative way. Specifically, nodes use the 6top protocol to negotiate network resources with their neighbors. In this paper, we investigate the performance of 6top protocol through a set of experimental measurements on a testbed. We show that the time required to complete a 6top transaction in a real environment is not negligible, as often assumed in many studies. In addition, a …

Sensor System And Method For Cognitive Health Assessment, Debraj De, Sajal K. Das, Mignon Makos

Computer Science Faculty Research & Creative Works

Sensors arranged on a chair on which a subject is seated detect a physical characteristic of the subject during administration of a cognitive health assessment. An assessment processor coupled to the sensors executes computer-executable instructions causing the processor to determine a contemporaneous reaction corresponding to each of the questions as a function of the detected physical characteristic. And the subject is assigned a cognitive health assessment score based on the subject's answers and determined reactions.

Learning Temporal Information From A Single Image For Au Detection, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

Automatic Facial Action Units (AUs) detection is the recognition of the facial appearance changes caused by the contraction or relaxation of one or more related facial muscles. Compared to the sequence-based methods, a decreased performance is observed for the static image-based AU detection, due to the loss of temporal information. To solve this problem, we propose a novel method that implicitly learns temporal information from a single image for AU detection by adding a hidden optical-flow layer to concatenate two Convolutional Neural Networks (CNNs) models: optical-flow net (OF-Net) and AU detection net (AU-Net). The OF-Net is designed to estimate the …