Computer Sciences Commons^™

An Exploratory Analysis Of Mobile Security Tools, Hossain Shahriar, Md Arabin Talukder, Md Saiful Islam

KSU Proceedings on Cybersecurity Education, Research and Practice

The growing market of the mobile application is overtaking the web application. Mobile application development environment is open source, which attracts new inexperienced developers to gain hands on experience with applicationn development. However, the security of data and vulnerable coding practice is an issue. Among all mobile Operating systems such as, iOS (by Apple), Android (by Google) and Blackberry (RIM), Android dominates the market. The majority of malicious mobile attacks take advantage of vulnerabilities in mobile applications, such as sensitive data leakage via the inadvertent or side channel, unsecured sensitive data storage, data transition and many others. Most of these …

Iot: Challenges In Information Security Training, Lech J. Janczewski, Gerard Ward

KSU Proceedings on Cybersecurity Education, Research and Practice

Both consumers and businesses are rapidly adopting IoT premised on convenience and control. Industry and academic literature talk about billions of embedded IoT devices being implemented with use-cases ranging from smart speakers in the home, to autonomous trucks, and trains operating in remote industrial sites. Historically information systems supporting these disparate use-cases have been categorised as Information Technology (IT) or Operational Technology (OT), but IoT represents a fusion between these traditionally distinct information security models.

This paper presents a review of IEEE and Elsevier peer reviewed papers that identifies the direction in IoT education and training around information security. It …

Proposal For A Joint Cybersecurity And Information Technology Management Program, Christopher Simpson, Debra Bowen, William Reid, James Juarez

KSU Proceedings on Cybersecurity Education, Research and Practice

Cybersecurity and Information Technology Management programs have many similarities and many similar knowledge, skills, and abilities are taught across both programs. The skill mappings for the NICE Framework and the knowledge units required to become a National Security Agency and Department of Homeland Security Center of Academic Excellence in Cyber Defense Education contain many information technology management functions. This paper explores one university’s perception on how a joint Cybersecurity and Information Technology Management program could be developed to upskill students to be work force ready.

Adversarial Thinking: Teaching Students To Think Like A Hacker, Frank Katz

KSU Proceedings on Cybersecurity Education, Research and Practice

Today’s college and university cybersecurity programs often contain multiple laboratory activities on various different hardware and software-based cybersecurity tools. These include preventive tools such as firewalls, virtual private networks, and intrusion detection systems. Some of these are tools used in attacking a network, such as packet sniffers and learning how to craft cross-site scripting attacks or man-in-the-middle attacks. All of these are important in learning cybersecurity. However, there is another important component of cybersecurity education – teaching students how to protect a system or network from attackers by learning their motivations, and how they think, developing the students’ “abilities to …

Collaborating On Machine Reading: Training Algorithms To Read Complex Collections, Carrie M. Pirmann, Brian R. King, Bhagawat Acharya, Katherine M. Faull

Bucknell University Digital Scholarship Conference

Interdisciplinary collaboration between two faculty members in the humanities and computer science, a research librarian, and an undergraduate student has led to remarkable results in an ongoing international DH research project that has at its core 18th century manuscripts. The corpus stems from a vast collection of archival materials held by the Moravian Church in the UK, Germany, and the US. The number of pages to be transcribed, differences in handwriting styles, paper quality, and original language pose enormous problems for the feasibility of human transcription. This presentation will review the hypothesis, process, and findings of a summer research project …

Internet Core Functions: Security Today And Future State, Jeffrey Jones

KSU Proceedings on Cybersecurity Education, Research and Practice

Never in the history of the world has so much trust been given to something that so few understand. Jeff reviews three core functions of the Internet along with recent and upcoming changes that will impact security and the world.

Preparing For Tomorrow By Looking At Yesterday, Peter Dooley

KSU Proceedings on Cybersecurity Education, Research and Practice

Why do we learn? Why do we study history? Why do we research the work of others? The answer is that there is value today in what was already learned and experienced, successes and failures. Mr. Dooley, a 25-year security professional and 20-year hospitality executive, will share his experiences and how our history in security will help us in thinking about our future.

A Computational Model For Recovery From Brain Injury, Wayne Wakeland

Systems Science Friday Noon Seminar Series

A computational simulation model calculates recovery trajectories following traumatic brain injury (TBI). Prior publications include a multi-scale framework for studying concussion and a systems-level causal loop diagram (CLD) and discussion of feedback processes. The scope of the computational model goes beyond concussion to include all severities of TBI. A set of first order ordinary differential equations and their associated parameters determines recovery trajectories. While highly speculative, the model serves to demonstrate the potential utility of computational models in this context. Much more research will be needed to create a properly supported research model that could be used for clinical trial …

Concolic Testing Heap-Manipulating Programs, Long H. Pham, Quang Loc Le, Quoc-Sang Phan, Jun Sun

Research Collection School Of Computing and Information Systems

Concolic testing is a test generation technique which works effectively by integrating random testing generation and symbolic execution. Existing concolic testing engines focus on numeric programs. Heap-manipulating programs make extensive use of complex heap objects like trees and lists. Testing such programs is challenging due to multiple reasons. Firstly, test inputs for such program are required to satisfy non-trivial constraints which must be specified precisely. Secondly, precisely encoding and solving path conditions in such programs are challenging and often expensive. In this work, we propose the first concolic testing engine called CSF for heap-manipulating programs based on separation logic. CSF …

Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher

Conference papers

First Story Detection (FSD) requires a system to detect the very first story that mentions an event from a stream of stories. Nearest neighbour-based models, using the traditional term vector document representations like TF-IDF, currently achieve the state of the art in FSD. Because of its online nature, a dynamic term vector model that is incrementally updated during the detection process is usually adopted for FSD instead of a static model. However, very little research has investigated the selection of hyper-parameters and the background corpora for a dynamic model. In this paper, we analyse how a dynamic term vector model …

Development Of Spatiotemporal Congestion Pattern Observation Model Using Historical And Near Real Time Data, Betty Kretlow

Master of Science in Computer Science Theses

Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was …

Integrating Mathematics And Biology In The Classroom: A Compendium Of Case Studies And Labs, Becky Sanft, Anne Walter

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Adaptive Randomized Rounding In The Big Parsimony Problem, Sangho Shim, Sunil Chopra, Eunseok Kim

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Quantifying Pollen Traits To Build A Mathematical Model Of Pollen Competition - A Mathematician's Perspective, Montana Ferita, Julie Fucarino, Alex Capaldi, Charlotte Beckford

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Period Drift In A Neutrally Stable Stochastic Oscillator, Kevin Sanft

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

An Agent-Based Model Of An Endangered Florida Tillansia Utriculata Population, Erin N. Bodine, Alexandra Campbell, Anna C. Kula

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Automatic Inference Of Causal Reasoning Chains From Student Essays, Simon Mark Hughes

College of Computing and Digital Media Dissertations

While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features …

Machine Learning To Support An Interactive Theorem Prover, Salman Haider, Andy Le, Echo Wu, Brian T. Howard

Annual Student Research Poster Session

An Interactive Theorem Prover (ITP) is a computer program that can assist a human in creating a proof of a mathematical theorem or the correctness of a piece of software. At each step in the proof, the human has the computer apply a chosen "tactic" to attempt to make progress toward the goal. We are exploring the possibility of using Machine Learning (ML) to assist in this tactic selection. Building on recent work at UC Berkeley and Princeton, where they adapted the Coq ITP so that it could interface with ML libraries via the Python scripting language, we have been …

Visualizing The Mean And The Standard Deviation Using R/Rstudio Shiny Package, Rachel Hufnagel, Ziyi (Amy) Chen, Mamunur Rashid

Annual Student Research Poster Session

Many of us have experienced an unpleasant situation in which only the mean and the standard deviation of a data set are reported, but we are expected to know everything about the dataset as if those two values were all we needed to know. We would learn so much more if there were easy ways to create and share graphical representations and interpretations of the entire raw data. Here we not only explain what the mean and the standard deviation tell us about a data set, but also describe how to include additional information on the data. We utilize the …

Cybersecurity Framework And Algorithms For Prioritized Vulnerability Mitigation (Cyfer): An Adoption To Blockchain Systems, Sri Nikhil Gupta Gourisetti

Theses and Dissertations

Cybersecurity vulnerability assessment tools, frameworks, methodologies, and processes are commonly used to understand the cybersecurity maturity and posture of a system or a facility. Those tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy, and the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we used multi-criteria decision analysis (MCDA) techniques to develop a framework and a software application family called the Cybersecurity Framework and Algorithms …

Multi-User Virtual Reality With Lenticular Lenses, Juan Sebastian Munoz Arango

Theses and Dissertations

One of the all time issues with Virtual Reality systems regardless if they are head-mounted or projection based is that they can only provide perspective correctness to one user. This limitation not only affects collaborative work which is nowadays the standard, but it has also been shown that not having a perspective correct views increases collaboration times. Different approaches have been presented throughout the past years for generating perspective correct images to several users. One of such approaches relies on bending the light to generate perspective correct images. Lenticular printing is a technique that uses the properties of small lenses …

Experiences Of Using Intelligent Virtual Assistants By Visually Impaired Students In Online Higher Education, Michele R. Forbes

USF Tampa Graduate Theses and Dissertations

In today’s world, the attainment of higher education impacts the acquisition of competitive employment and, thus, quality of life. As a group, persons with disabilities continually fall behind others in such academic progress, requiring new efforts to support their earning of advanced credentials. Though highly beneficial for these individuals, obtaining a degree comes with elevated levels of stress. As enrollment of students with disabilities grows in all formats of higher education, those involved must understand the stress endured by these students and how to diminish it. Theories speculate that technology, such as intelligent virtual assistants, may be a viable tool …

Artificial Intelligence, Real Impact, Singapore Management University

Perspectives@SMU

AI use in China continues to push innovation envelopes, but technology must be utilised and updated with expert advice

Why A Classification Based On Linear Approximation To Dynamical Systems Often Works Well In Nonlinear Cases, Julio Urenda, Vladik Kreinovich

Departmental Technical Reports (CS)

It can be proven that linear dynamical systems exhibit either stable behavior, or unstable behavior, or oscillatory behavior, or transitional behavior. Interesting, the same classification often applies to nonlinear dynamical systems as well. In this paper, we provide a possible explanation for this phenomenon, i.e., we explain why a classification based on linear approximation to dynamical systems often works well in nonlinear cases.

Development Of An Autonomous Aerial Toolset For Agricultural Applications, Terrance Life

Mahurin Honors College Capstone Experience/Thesis Projects

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will …

Multilevel Combinatorial Optimization Across Quantum Architectures, Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Christian F.A. Negre, Susan M. Mniszewski, Yuri Alexeev, Ilya Safro

Publications

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the Post Moore's law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid …

Effective Capacity In Wireless Networks: A Comprehensive Survey, Muhammad Amjad, Mubashir Husain Rehmani, Leila Musavian

Publications

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical layer channel models, however, do not explicitly consider quality of service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing …

Adaptive Feature Engineering Modeling For Ultrasound Image Classification For Decision Support, Hatwib Mugasa

Doctoral Dissertations

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually …

Feature Space Modeling For Accurate And Efficient Learning From Non-Stationary Data, Ayesha Akter

Doctoral Dissertations

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and …

Machine Learning Based Ultra High Carbon Steel Image Segmentation, Sumith Kuttiyil Suresh

Theses and Dissertations

Mechanical and structural properties of ultra-high carbon steel are determined by their microstructures composed of constituents such as pearlite and spheroidites. Locating micro constituents and quantitatively measuring its presence is key for material researchers to study the physical properties of the carbon steel materials. This micrograph analysis is currently done manually and subjectively by material scientists, which is tedious and time-consuming. Here we propose to apply the image segmentation algorithm called U-Net to achieve automated labeling of steel microstructures on a subset of ultra- high carbon steel image dataset containing pearlite and spheroidite as the primary micro constituents. Our work …