Computer Sciences Commons^™

A Web Application For The Remote Control Of Multiple Unmanned Aerial Vehicles, Riccardo Musmeci, Ken Goss, Simone Silvestri, Giuseppe Lo Re

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) are receiving an increasing attention from the research and industry community, and today they are adopted for several civilian and military applications. However, state of the art technologies is still based on a single UAV either directly controlled by the human operator or supervised through the manual definition of a flight plan. As a result, scalability is still a significant limitation for such systems, especially when large areas need to be monitored. In this paper we propose a web-based application for the control of multiple UAVs. The application has three layers. The first layer allows the …

Multi-Modality Empowered Network For Facial Action Unit Detection, Peng Liu, Zheng Zhang, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

This paper presents a new thermal empowered multi-task network (TEMT-Net) to improve facial action unit detection. Our primary goal is to leverage the situation that the training set has multi-modality data while the application scenario only has one modality. Thermal images are robust to illumination and face color. In the proposed multi-task framework, we utilize both modality data. Action unit detection and facial landmark detection are correlated tasks. To utilize the advantage and the correlation of different modalities and different tasks, we propose a novel thermal empowered multi-task deep neural network learning approach for action unit detection, facial landmark detection …

Iq2s'19 - 10th International Workshop On Information Quality And Quality Of Service For Pervasive Computing - Welcome And Committees, Sajal K. Das

Computer Science Faculty Research & Creative Works

No abstract provided.

Parlech: Parallel Long-Read Error Correction With Hadoop, Arghya Kusum Das, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Long-read sequencing is emerging as a promising sequencing technology because it can tackle the short length limitation of second-generation sequencing, which has dominated the sequencing market in past years. However, it has substantially higher error rates compared to short-read sequencing (e.g., 13% vs. 0.1%), and its sequencing cost per base is typically more expensive than that of short-read sequencing. To address these limitations, we present a distributed hybrid error correction framework, called ParLECH, that is scalable and cost-efficient for PacBio long reads. For correcting the errors in the long reads, ParLECH utilizes the Illumina short reads that have the low …

Facial Expression Recognition By De-Expression Residue Learning, Huiyuan Yang, Umur Ciftci, Lijun Yin

Computer Science Faculty Research & Creative Works

A facial expression is a combination of an expressive component and a neutral component of a person. In this paper, we propose to recognize facial expressions by extracting information of the expressive component through a de-expression learning procedure, called De-expression Residue Learning (DeRL). First, a generative model is trained by cGAN. This model generates the corresponding neutral face image for any input face image. We call this procedure de-expression because the expressive information is filtered out by the generative model; however, the expressive information is still recorded in the intermediate layers. Given the neutral face image, unlike previous works using …

Exploring Best Lossy Compression Strategy By Combining Sz With Spatiotemporal Decimation, Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

In today’s extreme-scale scientific simulations, vast volumes of data are being produced such that the data cannot be accommodated by the parallel file system or the data writing/ reading performance will be fairly low because of limited I/O bandwidth. In the past decade, many snapshot-based (or space-based) lossy compressors have been developed, most of which rely on the smoothness of the data in space. However, the simulation data may get more and more complicated in space over time steps, such that the compression ratios decrease significantly. In this paper, we propose a novel, hybrid lossy compression method by leveraging spatiotemporal …

Improving Error-Bounded Compression For Cosmological Simulation, Sihuan Li, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Cosmological simulations may produce extremely large amount of data, such that its successful run depends on large storage capacity and huge I/O bandwidth, especially in the exascale computing scale. Effective error-bounded lossy compressors with both high compression ratios and low data distortion can significantly reduce the total data size while guaranteeing the data valid for post-analysis. In this poster, we propose a novel, efficient compression model for cosmological N-body simulation framework, by combining the advantages of both space-based compression and time-based compression. The evaluation with a well-known cosmological simulation code shows that our proposed solution can get much higher compression …

Multicellular Models Bridging Intracellular Signaling And Gene Transcription To Population Dynamics, Mohammad Aminul Islam, Satyaki Roy, Sajal K. Das, Dipak Barua

Computer Science Faculty Research & Creative Works

Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular …

Phase Contrast Time-Lapse Microscopy Datasets With Automated And Manual Cell Tracking Annotations, Dai Fei Elmer Ker, Zhaozheng Yin, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these …

A Sensitivity Analysis For Mixed Criticality: Trading Criticality With Computational Resource, Luca Santinelli, Zhishan Guo

Computer Science Faculty Research & Creative Works

Mixing workloads with multiple criticality levels raises challenges both in timing analysis and schedulability analysis. The timing models have to characterize the different behaviors that real-time tasks can experience under the various criticality modes. Instead, the schedulability analysis has to combine every task and task interactions providing several guarantees, depending on the criticality level demanded at runtime. With this work, at first, we propose representations to model every possible system criticality mode as a combination of task criticality modes. A set of bounding functions is obtained, a bound for each mode combination thus corresponding to a system criticality level. Secondly, …

From Tag To Protect: A Tag-Driven Policy Recommender System For Image Sharing, Anna Cinzia Squicciarini, Andrea Novelli, Dan Lin, Cornelia Caragea, Haoti Zhong

Computer Science Faculty Research & Creative Works

Sharing images on social network sites has become a part of daily routine for more and more online users. However, in face of the considerable number of images shared online, it is not a trivial task for a person to manually configure proper privacy settings for each of the images that he/she uploaded. The lack of proper privacy protection during image sharing could raise many potential privacy breaches of people's private lives that they are not aware of. In this work, we propose a privacy setting recommender system to help people effortlessly set up the privacy settings for their online …

Multi-Task Allocation In Mobile Crowd Sensing With Individual Task Quality Assurance, Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Haoyi Xiong, Chao Chen, Qin Lv, Zhaopeng Qiu

Computer Science Faculty Research & Creative Works

Task allocation is a fundamental research issue in mobile crowd sensing. While earlier research focused mainly on single tasks, recent studies have started to investigate multi-task allocation, which considers the interdependency among multiple tasks. A common drawback shared by existing multi-task allocation approaches is that, although the overall utility of multiple tasks is optimized, the sensing quality of individual tasks may become poor as the number of tasks increases. To overcome this drawback, we re-define the multi-task allocation problem by introducing task-specific minimal sensing quality thresholds, with the objective of assigning an appropriate set of tasks to each worker such …

A Distributed Semi-Supervised Platform For Dnase-Seq Data Analytics Using Deep Generative Convolutional Networks, Shayan Shams, Richard Platania, Joohyun Kim, Jian Zhang, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

A deep learning approach for analyzing DNase-seq datasets is presented, which has promising potentials for unraveling biological underpinnings on transcription regulation mechanisms. Further understanding of these mechanisms can lead to important advances in life sciences in general and drug, biomarker discovery, and cancer research in particular. Motivated by recent remarkable advances in the field of deep learning, we developed a platform, Deep Semi-Supervised DNase-seq Analytics (DSSDA). Primarily empowered by deep generative Convolutional Networks (ConvNets), the most notable aspect is the capability of semi-supervised learning, which is highly beneficial for common biological settings often plagued with a less sufficient number of …

Work-In-Progress: Rws - A Roulette Wheel Scheduler For Preventing Execution Pattern Leakage, Ying Zhang, Lingxiang Wang, Wei Jiang, Zhishan Guo

Computer Science Faculty Research & Creative Works

Many real-time systems are safety-critical, where reliability is crucial. Under traditional scheduling mechanism, the execution patterns of the tasks on such system can be easily derived from side-channel attacks, such that attackers can launch short high-priority tasks at critical instants which may cause deadline miss for high-critical tasks. In order to protect the system from such kind of attacks, this paper proposes the roulette wheel scheduler (RWS) to randomize the task execution pattern. Under RWS, probabilities will be assigned to each task at predefined scheduling points, and the choice for execution is randomized, such that the execution pattern is no …

Gpu-Accelerated Large-Scale Genome Assembly, Sayan Goswami, Kisung Lee, Shayan Shams, Seung Jong Park

Computer Science Faculty Research & Creative Works

Spurred by a widening gap between hardware accelerators and traditional processors, numerous bioinformatics applications have harnessed the computing power of GPUS and reported substantial performance improvements compared to their CPU-based counterparts. However, most of these GPU-based applications only focus on the read alignment problem, while the field of de novo assembly still relies mostly on CPU-based solutions. This is primarily due to the nature of the assembly workload which is not only compute-intensive but also extremely data-intensive. Such workloads require large memories, making it difficult to adapt them to use GPUS with their limited memory capacities. To the best of …

Optical Lens Attack On Monocular Depth Estimation For Autonomous Driving, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Weikang Ding, Ziqi Zhang, Hayder Radha

Computer Science Faculty Research & Creative Works

Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a single camera image. This estimation guides essential driving decisions, such as braking before an obstacle or changing lanes to avoid collisions. In this paper, we explore vulnerabilities of MDE algorithms in AD systems, presenting 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘, a novel physical attack that strategically places optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘 encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to …

A Study Of The Genetic Algorithm Parameters For Solving Multi-Objective Travelling Salesman Problem, Romit S. Beed, Sunita Sarkar, Arindam Roy, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

The objective of this work is to present a solution to a multiple-objective optimization problem using genetic algorithms (GA). Generally, the objectives (minimizing cost, maximizing performance, reducing carbon footprints, maximizing profit) are conflicting for multiple-objective problems, hindering concurrent optimization of each objective. A bi-objective traditional combinatorial optimization of Travelling Salesman Problem is undertaken named as the Multi-Objective Travelling Salesman Problem (MTSP). The two objectives are minimization of the distance travelled by the salesman and minimization of the time taken to travel. The purpose of this paper is to model the problem as a single objective optimization problem using the weighted …

Mac Layer Misbehavior Detection Using Time Series Analysis, Maggie X. Cheng, Yi Ling, Wei Biao Wu

Computer Science Faculty Research & Creative Works

This paper presents a solution to the real-time detection of MAC layer misbehaviors in IEEE 802.11 networks. Among the wide range of misbehaviors, we focus on the sender side selfish behavior that creates a channel- capturing effect by using favorable parameters, and the receiver side selfish behavior that does not respond with CTS and ACK upon receiving RTS and data packets, which clears the channel for itself and causes its sender to waste resources. These misbehaviors are subtle to detect and yet can undermine the performance of the well-behaved nodes significantly. This paper shows a powerful real-time detection method that …

Towards Distributed Cyberinfrastructure For Smart Cities Using Big Data And Deep Learning Technologies, Shayan Shams, Sayan Goswami, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure …

The Automated Design Of Probabilistic Selection Methods For Evolutionary Algorithms, Samuel N. Richter, Daniel R. Tauritz

Computer Science Faculty Research & Creative Works

Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection methods exist, each providing a unique relationship between the fitnesses of individuals in a population and their chances of selection. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance may be expected by tuning an EA's selection algorithm to the problem at …

Evolution Of Network Enumeration Strategies In Emulated Computer Networks, Sean Harris, Eric Michalak, Kevin Schoonover, Adam Gausmann, Hannah Reinbolt, Joshua Herman, Daniel R. Tauritz, Chris Rawlings, Aaron Scott Pope

Computer Science Faculty Research & Creative Works

Successful attacks on computer networks today do not often owe their victory to directly overcoming strong security measures set up by the defender. Rather, most attacks succeed because the number of possible vulnerabilities are too large for humans to fully protect without making a mistake. Regardless of the security elsewhere, a skilled attacker can exploit a single vulnerability in a defensive system and negate the benefits of those security measures. This paper presents an evolutionary framework for evolving attacker agents in a real, emulated network environment using genetic programming, as a foundation for coevolutionary systems which can automatically discover and …

Automated Design Of Network Security Metrics, Aaron Scott Pope, Daniel R. Tauritz, Robert Morning, Alexander D. Kent

Computer Science Faculty Research & Creative Works

Many abstract security measurements are based on characteristics of a graph that represents the network. These are typically simple and quick to compute but are often of little practical use in making real-world predictions. Practical network security is often measured using simulation or real-world exercises. These approaches better represent realistic outcomes but can be costly and time-consuming. This work aims to combine the strengths of these two approaches, developing efficient heuristics that accurately predict attack success. Hyper-heuristic machine learning techniques, trained on network attack simulation training data, are used to produce novel graph-based security metrics. These low-cost metrics serve as …

A Network Tomography Approach For Traffic Monitoring In Smart Cities, Ruoxi Zhang, Sara Newman, Marco Ortolani, Simone Silvestri

Computer Science Faculty Research & Creative Works

Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. We model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. We specifically deal with the potential …

Multi-Objective Optimization Based Allocation Of Heterogeneous Spatial Crowdsourcing Tasks, Liang Wang, Zhiwen Yu, Qi Han, Bin Guo, Haoyi Xiong

Computer Science Faculty Research & Creative Works

With the rapid development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, which refers to recruiting mobile workers to perform location-based tasks, has gained emerging interest from both research communities and industries. In this paper, we consider a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task has a valid duration, operation complexity, budget limitation, and the number of required workers. Each volunteer worker completes assigned tasks while conducting his/her routine tasks. The system has a desired task probability coverage and budget constraint. Under this scenario, we investigate an important problem, namely heterogeneous spatial crowdsourcing …

Improving Performance Of Iterative Methods By Lossy Checkponting, Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging …

Identity-Adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks, Huiyuan Yang, Zheng Zhang, Lijun Yin

Computer Science Faculty Research & Creative Works

Subject variation is a challenging issue for facial expression recognition, especially when handling unseen subjects with small-scale labeled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative …

Design Of Robust And Efficient Topology Using Enhanced Gene Regulatory Networks, Satyaki Roy, Vijay K. Shah, Sajal K. Das

Computer Science Faculty Research & Creative Works

Biological networks are characterized by their inherent robustness against component failures. Gene regulatory networks (GRNs) are biological networks with graph properties contributing to their innate functional robustness. In this paper, we first propose a three-tier topological characterization to study the graph properties of GRN, namely scale free out-degree distribution, low graph density, and abundance of subgraphs, called motifs. We then present a novel edge rewiring mechanism, consisting of edge addition and deletion algorithms, to remedy its vulnerability against failure of well-connected nodes while preserving its graph properties. We discuss the preferential attachment growth-based edge addition and greedy edge deletion. We …

Heterogeneous Activity Causes A Nonlinear Increase In The Group Energy Use Of Ant Workers Isolated From Queen And Brood, Nolan Ferral, Kyara Holloway, Mingzhong Li, Zhaozheng Yin, Chen Hou

Computer Science Faculty Research & Creative Works

Increasing evidence has shown that the energy use of ant colonies increases sublinearly with colony size so that large colonies consume less per capita energy than small colonies. It has been postulated that social environment (e.g., in the presence of queen and brood) is critical for the sublinear group energetics, and a few studies of ant workers isolated from queens and brood observed linear relationships between group energetics and size. In this paper, we hypothesize that the sublinear energetics arise from the heterogeneity of activity in ant groups, that is, large groups have relatively more inactive members than small groups. …

Software Engineering: Guest Editor's Introduction, Bruce M. Mcmillin

Computer Science Faculty Research & Creative Works

Engineering complex systems requires an extensive technical and semantic knowledge. Because of their complexity, components of these systems can interact in unpredictable ways -- sequentially and concurrently….

The three papers in this theme issue have the potential to address the design challenges of complex systems spanning multiple domains and interacting in potentially unexpected ways. The knowledge required goes beyond basic computing concepts and design, to include an examination of interactions and multiple aspects of the application domain. We hope you find these articles interesting and insightful.

A Group-Based Energy Harvesting Mac Protocol With Ap Scheduling In Machine-To-Machine Networks, Ce Zhou, Yunmin Kim, Tae-Jin Lee

Computer Science Faculty Research & Creative Works

In Machine-to-Machine (M2M) communications, an enormous number of devices are subject to the battery limitation. Recently, an attractive energy harvesting technology called energy beamforming has shown great potential to make wireless devices self-powering. Motivated by this, according to the IEEE 802.11ah, a group-based energy harvesting Medium Access Control (MAC) protocol with the strategy of AP scheduling is designed in the paper to address the energy shortage without much reduction in system throughput. In the proposed protocol, nodes first contend for the transmission opportunities, and then data and energy transfer simultaneously by the AP scheduling scheme. Multiple Power Beacons (PBs) are …