Computer Sciences Commons^™

Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou

Research Collection School Of Computing and Information Systems

Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional …

Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr

Research Collection School Of Computing and Information Systems

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …

Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority …

A Study Of Face Embedding In Face Recognition, Khanh Duc Le

Master's Theses

Face Recognition has been a long-standing topic in computer vision and pattern recognition field because of its wide and important applications in our daily lives such as surveillance system, access control, and so on. The current modern face recognition model, which keeps only a couple of images per person in the database, can now recognize a face with high accuracy. Moreover, the model does not need to be retrained every time a new person is added to the database.

By using the face dataset from Digital Democracy, the thesis will explore the capability of this model by comparing it with …

Supported Programming For Beginning Developers, Andrew Gilbert

Master's Theses

Testing code is important, but writing test cases can be time consuming, particularly for beginning programmers who are already struggling to write an implementation. We present TestBuilder, a system for test case generation which uses an SMT solver to generate inputs to reach specified lines in a function, and asks the user what the expected outputs would be for those inputs. The resulting test cases check the correctness of the output, rather than merely ensuring the code does not crash. Further, by querying the user for expectations, TestBuilder encourages the programmer to think about what their code ought to do, …

Efficient And Scalable Event Tracing, Rupika Dikkala

University Honors Theses

In this work, I demonstrate that a time series database can be utilized to store Open Trace Format 2 (OTF2) file metadata for common trace events efficiently and scalably. This paper examines the efficacy of storing event trace data in a time series database, and investigates associated performance overhead compared to the state of the art method using OTF2 trace files. The sample traces used in this project are generated from a parallel hydrodynamic modeling code, Lulesh, developed at Lawrence Livermore National Laboratory. In my approach, I first cache common event trace metadata in InfluxDB, a contemporary time series database. …

Neuroevolutional Methods For Decision Support Under Uncertainty, Nina Komleva, Olga Khlopkova, Matthew He

Mathematics Faculty Articles

The article presents a comparative analysis of the fundamental neuroevolutional methods, which are widely applied for the intellectualization of the decision making support systems under uncertainty. Based on this analysis the new neuroevolutionary method is introduced. It is intended to modify both the topology and the parameters of the neural network, and not to impose additional constraints on the individual. The results of the experimental evaluation of the performance of the methods based on the series of benchmark tasks of adaptive control, classification and restoration of damaged data are carried out. As criteria of the methods evaluation the number of …

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Kaggle And Click-Through Rate Prediction, Todd W. Neller

Computer Science Faculty Publications

Neller presented a look at Kaggle.com, an online Data Science and Machine Learning learning community, as a place to seek rapid, experiential peer education for most any Data Science topic. Using the specific challenge of Click-Through Rate Prediction (CTRP), he focused on lessons learned from relevant Kaggle competitions on how to perform CTRP.

Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica Wiredja, Daniela Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance

Computer Science Faculty Publications

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, …

Astria Ontology: Open, Standards-Based, Data-Aggregated Representation Of Space Objects, Jennie Wolfgang, Kathleen Krysher, Michael Slovenski, Unmil P. Karadkar, Shiva Iyer, Moriba K. Jah

Space Traffic Management Conference

The necessity for standards-based ontologies for long-term sustainability of space operations and safety of increasing space flights has been well-established [6, 7]. Current ontologies, such as DARPA’s OrbitOutlook [5], are not publicly available, complicating efforts for their broad adoption. Most sensor data is siloed in proprietary databases [2] and provided only to authorized users, further complicating efforts to create a holistic view of resident space objects (RSOs) in order to enhance space situational awareness (SSA).

The ASTRIA project is developing an open data model with the goal of aggregating data about RSOs, parts, space weather, and governing policies in order …

Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell

Journal of Digital Forensics, Security and Law

Throughout the digital forensic community, chip-off analysis provides examiners with a technique to obtain a physical acquisition from locked or damaged digital device. Thermal based chip-analysis relies upon the application of heat to remove the flash memory chip from the circuit board. Occasionally, a flash memory chip fails to successfully read despite following similar protocols as other flash memory chips. Previous research found the application of high temperatures increased the number of bit errors present in the flash memory chip. The purpose of this study is to analyze data collected from chip-off analyses to determine if a statistical difference exists …

Randomized Parameterized Algorithms For The Kidney Exchange Problem, Mugang Lin, Jianxin Wang, Qilong Feng, Bin Fu

Computer Science Faculty Publications

In order to increase the potential kidney transplants between patients and their incompatible donors, kidney exchange programs have been created in many countries. In the programs, designing algorithms for the kidney exchange problem plays a critical role. The graph theory model of the kidney exchange problem is to find a maximum weight packing of vertex-disjoint cycles and chains for a given weighted digraph. In general, the length of cycles is not more than a given constant L (typically 2 L 5), and the objective function corresponds to maximizing the number of possible kidney transplants. In this paper, we study the …

2019 February 22 - Computation And Research In Data Science (Cards) Minutes, Computation And Research In Data Science, East Tennessee State University

Computation and Research in Data Science (CaRDS) Board Meeting Minutes

No abstract provided.

Bandwidth Scheduling For Big Data Transfer With Deadline Constraint Between Data Centers, Aiqin Hou, Chase Q. Wu, Dingyi Fang, Liudong Zuo, Michelle Zhu, Xiaoyang Zhang, Ruimin Qiao, Xiaoyan Yin

Department of Computer Science Faculty Scholarship and Creative Works

An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for the movement of big data between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed QoS for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data …

Detecting Rtl Trojans Using Artificial Immune Systems And High Level Behavior Classification, Farhath Zareen

USF Tampa Graduate Theses and Dissertations

Security assurance in a computer system can be viewed as distinguishing between self and non-self. Artificial Immune Systems (AIS) are a class of machine learning (ML) techniques inspired by the behavior of innate biological immune systems, which have evolved to accurately classify self-behavior from non-self-behavior. This work aims to leverage AIS-based ML techniques for identifying certain behavioral traits in high level hardware descriptions, including unsafe or undesirable behaviors, whether such behavior exists due to human error during development or due to intentional, malicious circuit modifications, known as hardware Trojans, without the need fora golden reference model. We explore the use …

Allosteric Mechanism Of The Circadian Protein Vivid Resolved Through Markov State Model And Machine Learning Analysis, Hongyu Zhou, Zheng Dong, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an …

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Near Earth Space Object Detection Using Parallax As Multi-Hypothesis Test Criterion, Joseph C. Tompkins, Stephen C. Cain, David J. Becker

Faculty Publications

The US Strategic Command (USSTRATCOM) operated Space Surveillance Network (SSN) is tasked with Space Situational Awareness (SSA) for the U.S. military. This system is made up of Electro-Optic sensors, such as the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) and RADAR based sensors, such as the Space Fence Gaps. They remain in the tracking of Resident Space Objects (RSO’s) in Geosynchronous Orbits (GEO), due to limitations of SST and GEODSS system implementation. This research explores a reliable, ground-based technique used to quickly determine an RSO’s altitude from a single or limited set of observations. Implementation of such sensors into the SSN …

Automatic Acquisition Of Annotated Training Corpora For Test-Code Generation, Magdalena Kacmajor, John D. Kelleher

Articles

Open software repositories make large amounts of source code publicly available. Potentially, this source code could be used as training data to develop new, machine learning-based programming tools. For many applications, however, raw code scraped from online repositories does not constitute an adequate training dataset. Building on the recent and rapid improvements in machine translation (MT), one possibly very interesting application is code generation from natural language descriptions. One of the bottlenecks in developing these MT-inspired systems is the acquisition of parallel text-code corpora required for training code-generative models. This paper addresses the problem of automatically synthetizing parallel text-code corpora …

Single Image Super-Resolution, Yujing Song

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a methodology known as generative adversarial net- work (GAN) which is better at adding texture details to the high resolution output.

Smart Parking Systems Design And Integration Into Iot, Charles M. Menne

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper looks at two smart parking reservation algorithms, and examines the ongoing efforts to connect smart systems of different domains in a city's infrastructure. The reservation algorithms are designed to improve the performance of smart parking systems. The first algorithm considers the distance between parking areas and the number of free parking spaces in determining a parking space. The second algorithm uses distance between parking areas and driver destination, parking price, and the number of unoccupied spaces for each parking area. Neither of these smart parking systems cover how they could fit into a larger scale smart system. As …

Requirements Practices In Software Startups, John D. Hoff

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

In a dynamic environment full of uncertainties in software startups, software development practices must be carefully approached. It is vital that startups determine the right time to make advancements and evolve their company to the next level. We will discuss the importance of requirements practices in startups and their impact on company culture, work environments, and product quality.

Robust Super-Resolution Approach To Source Localization In Ocean Waveguide Using Sparsity Constraint, Hai-Yan Song, Chang-Yi Yang, Ke-Jun Wang

Journal of Marine Science and Technology–Taiwan

Source localization in an ocean waveguide is a challenging problem because of the complexity of underwater acoustic propagation. Matched-field processing (MFP) has attracted considerable attention and has become a crucial technique for underwater acoustic source localization. Compressive sensing can achieve spatial sparsity, thus improving spatial resolution, by imposing penalties based on l1-norm. In this study, we developed a robust super-resolution approach for source localization in an ocean waveguide, which utilizes the inherent sparse structure of the spatial localization problem and underwater sound propagation principle. The proposed approach can be formulated as a sparse representation problem and further converted into a …

Optimal Design And Implementation Of Fractal Dipole Antenna Based On Arc Of Trigeminal Tree Structure, Xiaohong Zhang, Xiaoxiao Nan, Yixian Yang

Journal of System Simulation

Abstract: A RFID fractal dipole antenna is designed with the arc of the trigeminal tree structure, which is fed by simple microstrip balun to achieve better impedance matching within employing double parallel line structure and by using the dielectric substrate withε_r=4.4 and tan δ=0.035. The results of electromagnetic property simulation analysis of HFSS show that the return loss can reach 36dB, which is much higher than binary arc-shaped fractal antenna which is 16dB; and the impedance matching is very ideal. By physical processing and testing of the antenna, the measured results are in very good agreement with simulation …

Blind Separation Simulation System Of Sound Signals Based On Time-Frequency Analysis Of Short Time Fourier Transformation, Xiaorong Tong

Journal of System Simulation

Abstract: In order to improve the ability of real-time and separation accuracy, a blind separation algorithm based on time-frequency (TF) analysis of short time Fourier transformation is proposed, and the problems of underdetermined sound signal sorting can be solved effectively. The proposed method avoids some of the drawbacks, relaxes the assumption by allowing the sources to be TF-non-disjoint to a certain extent, and the algorithm can be achieved in the condition of TF-non-disjoint. This method was successfully applied to sound signal sorting system; the time domain waves of signals can be got; and the whole signal sorting process can be …

Evaluation Of Flow Line Structure Of Hospital With Spatial Cognitive Agent, Yunfeng Wang

Journal of System Simulation

Abstract: Hospital flow line is the result of the mutual adaptation of its spatial layout and business process, which should be regarded as an independent analysis unit in the spatial layout design. Aiming at the phenomenon that the flow lines have the characteristics of both space and flow and are difficult to analyze, a spatial cognitive process of agents is established by introducing structural elements from the spatial syntax analysis as the knowledge coding language of agents. Evaluation indexes of hospital flow line structure based on computational experiment are proposed and implemented. The framework of flow line analysis combining flow …

Evaluation And Modeling Of Visual Fatigue In 3d Display Based On Ecg, Danli Wang, Xinpan Yang, Yue Kang, Haichen Hu

Journal of System Simulation

Abstract: This study investigates and models the correlation between visual fatigue and ECG data by collecting the ECG data of the participants during watching 3D movies, combining with the subjective scores, the reaction time, and the questionnaires before and after the experiment. ECG data of 12 subjects were analyzed in both time and frequency domain. The results showed that subjective score and HR increased with the duration of viewing, while PNN50 decreased. Linear model of subjective score is established, and the R² of model is 0.930. Subjective questionnaires show significant changes in overall visual fatigue, dry eyes and nausea symptoms …

Centroidal Voronoi Tessellation With Local Optimization, Tianyu Ye, Yiqun Wang, Dongming Yan, Junhai Yong

Journal of System Simulation

Abstract: Centroidal Voronoi tessellation is a special geometric structure, which has many applications in various fields such as geographical information system, signal processing, mesh generation/optimization, visualization and so on. Due to the highly non-convex nature of the CVT energy function, the existing methods for computing CVT have several drawbacks, which always trap into local minima. We propose generation optimization and stochastic optimization schemes for further reducing the CVT energy. Experimental results show that the proposed method improves both quality and efficiency compared to the recent approaches.

Satellite Formation Keeping And Its Stability Analysis Based On Artificial Potential Field Method, Shengqing Yang, Wenyu Ye, Yubin He, Yabin Wan

Journal of System Simulation

Abstract: In this paper, a control method of satellite formation keeping is investigated based on artificial potential field. Since the N-body problem doesn’t have analytical solutions, a leader-follower formation is proposed as a restricted N-body problem. The stable structure has infinite possible pairs of coordinates in the inertial frame, therefore the Jacobian matrix of the corresponding error dynamic system at the equilibrium point is unsolvable. By introducing a transformation of coordinates from inertial frame to a specific reference frame, the stability analysis of error dynamic system is only related to the structure of formation. By the reduction of error …