Computer Sciences Commons^™

Treadmill Assisted Circumvention Of Wearable Sensors-Based Gait Authentication, Rajesh Kumar

Dissertations - ALL

Wearable sensor-based gait patterns are considered a promising means for future authentication systems. This dissertation examines whether circumvention of such systems can be accomplished by imitating sensor readings and providing external mechanical support. The specific machine that we used in the experiment was a digital treadmill, which provides a suitable platform for the human imitators to control, adjust and adapt several factors, such as speed, step-length, step-width, and thigh-lift that affect sensor readings. Moreover, it was easy for imitators to remember the gait factors' specific levels and repeat the learned pattern on-demand over a treadmill.

Two novel imitation-based attacks are …

Multi-Modal Self-Supervised Representation Learning For Earth Observation, Pallavi Jain, Bianca Schoen Phelan, Robert J. Ross

Conference papers

Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised …

Real-Time Adaptive Sensor Attack Detection And Recovery In Autonomous Cyber-Physical Systems, Francis Akowuah

Dissertations - ALL

Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks.Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision of the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there …

Augmented Human Machine Intelligence For Distributed Inference, Baocheng Geng

Dissertations - ALL

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and …

An Economical Method For Securely Disintegrating Solid-State Drives Using Blenders, Brandon J. Hopkins Phd, Kevin A. Riggle

Journal of Digital Forensics, Security and Law

Pulverizing solid-state drives (SSDs) down to particles no larger than 2 mm is required by the United States National Security Agency (NSA) to ensure the highest level of data security, but commercial disintegrators that achieve this standard are large, heavy, costly, and often difficult to access globally. Here, we present a portable, inexpensive, and accessible method of pulverizing SSDs using a household blender and other readily available materials. We verify this approach by pulverizing SSDs with a variety of household blenders for fixed periods of time and sieve the resulting powder to ensure appropriate particle size. Among the 6 household …

Learning To Learn Variational Semantic Memory, Xiantong Zhen, Yingjun Du, Huan Xiong, Cees G.M. Snoek, Ling Shao

Machine Learning Faculty Publications

In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. By doing so, it is able to accumulate long-term, general knowledge that enables it to learn new concepts of objects. We formulate memory recall as the variational inference of a latent memory variable from addressed contents, which offers a principled way to adapt the knowledge …

Representation Of Nonlinear Pseudo-Random Generators Using State-Space Equations, Raghad K. Salih

Emirates Journal for Engineering Research

The idea of research is a representation of the nonlinear pseudo-random generators using state-space equations that is not based on the usual description as shift register synthesis but in terms of matrices. Different types of nonlinear pseudo-random generators with their algorithms have been applied in order to investigate the output pseudo-random sequences. Moreover, two examples are given for conciliated the results of this representation.

Structured Latent Embeddings For Recognizing Unseen Classes In Unseen Domains, Shivam Chandhok, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Vineeth N. Balasubramanian, Fahad Shahbaz Khan, Ling Shao

Computer Vision Faculty Publications

The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues of semantic shift and domain shift, respectively. However, real-world applications often do not have constrained settings and necessitate handling unseen classes in unseen domains – a setting called Zero-shot Domain Generalization, which presents the issues of domain and semantic shifts simultaneously. In this work, we propose a novel approach that learns domain-agnostic structured latent embeddings by projecting images from different domains as well as class-specific …

Ai Output: A Human Condition That Should Not Be Protected Now, Or Maybe Ever, Xiao Wang

Chicago-Kent Journal of Intellectual Property

AI is usually considered to be a form of automatic and autonomous work, but when applied to the creation of literary and artistic works, challenges arise in deciding whether the AI is the de facto author of its output and whether AI outputs or AI-generated products should be protected under the copyright system. This article argues that these outputs should be human creations because the working principles of AI determine that AI functions merely as a mathematical tool applied by humans to not only conceive of but also to execute the creation of AI outputs. The creativity reflected in these …

Graph-Theoretic Partitioning Of Rnas And Classification Of Pseudoknots-Ii, Louis Petingi

Publications and Research

Dual graphs have been applied to model RNA secondary structures with pseudoknots, or intertwined base pairs. In previous works, a linear-time algorithm was introduced to partition dual graphs into maximally connected components called blocks and determine whether each block contains a pseudoknot or not. As pseudoknots can not be contained into two different blocks, this characterization allow us to efficiently isolate smaller RNA fragments and classify them as pseudoknotted or pseudoknot-free regions, while keeping these sub-structures intact. Moreover we have extended the partitioning algorithm by classifying a pseudoknot as either recursive or non-recursive in order to continue with our research …

A Deep Learning Approach For Forecasting Global Commodities Prices, Ahmed Saied Elberawi, Mohamed Belal Prof.

Future Computing and Informatics Journal

Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers and practitioners in statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, and GARCH) for a long time with varying accuracies. Deep learning provides more sophisticated and non-linear approximation that supersede traditional statistical methods in most cases. Deep learning methods require minimal features engineering compared to other methods; it adopts an end-to-end learning methodology. In addition, it can handle a huge amount of data and variables. Financial time series forecasting poses a challenge due to its high volatility and non-stationarity …

Entity Retrieval Using Fine-Grained Entity Aspects, Shubham Chatterjee, Laura Dietz

Computer Science Faculty Research & Creative Works

Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use in Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the …

Decoding Clinical Biomarker Space Of Covid-19: Exploring Matrix Factorization-Based Feature Selection Methods, Farshad Saberi-Movahed, Mahyar Mohammadifard, Adel Mehrpooya, Mohammad Rezaei-Ravari, Kamal Berahmand, Mehrdad Rostami, Saeed Karami, Mohammad Najafzadeh, Davood Hajinezhad, Mina Jamshidi, Farshid Abedi, Mahtab Mohammadifard, Elnaz Farbod, Farinaz Safavi, Mohammadreza Dorvash, Shahrzad Vahedi, Mahdi Eftekhari, Farid Saberi-Movahed, Iman Tavassoly

Publications and Research

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 …

A Health Elearning Ontology And Procedural Reasoning Approach For Developing Personalized Courses To Teach Patients About Their Medical Condition And Treatment, Martin Michalowski, Szymon Wilk, Wojtek Michalowski, Dympna O'Sullivan, Silvia Bonaccio, Enea Parimbelli, Marc Carrier, Grégoire Le Gal, Stephen Kingwell, Mor Peleg

Articles

We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific …

Efficient Metadata Lookup In Inline Deduplication Systems Leveraging Block Similarity, Rakesh Gururaj

Master's Projects

Data deduplication is a concept of physically storing a single instance of data by eliminating redundant copies to save the storage space. The adoption of deduplication is minimal in actively accessed primary storage because of its complexities, such as random access patterns to data and the need for quicker request response time. Most of the solutions designed for primary storage are offline and dependent on the concept of locality. This paper proposes an inline deduplication system with a Machine Learning based cache eviction policy to reduce the metadata overhead in the deduplication process, eliminate the redundant writes and improve the …

Classifier Performance Evaluation For Lightweight Ids Using Fog Computing In Iot Security, Belal Sudqi Khater, Ainuddin Wahid Abdul Wahab, Mohd Yamaniidna Idris, Mohammed Abdulla Hussain, Ashraf Ahmed Ibrahim, Mohammad Arif Amin, Hisham A. Shehadeh

All Works

In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In …

Diagnostic Accuracy Of Machine Learning Models To Identify Congenital Heart Disease: A Meta-Analysis, Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana A. Salam, Babar Hasan, Jai K. Das

Department of Paediatrics and Child Health

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.
Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to …

The Incubation Effect Among Students Playing An Educational Game For Physics, May Marie P. Talandron-Felipe, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

The incubation effect (IE) is a problem-solving phenomenon composed of three phases: pre-incubation where one fails to solve a problem; incubation, a momentary break where time is spent away from the unsolved problem; and post-incubation where the unsolved problem is revisited and solved. Literature on IE was limited to experiments involving traditional classroom activities. This initial investigation showed evidence of IE instances in a computer-based learning environment. This paper consolidates the studies on IE among students playing an educational game called Physics Playground and presents further analysis to examine the incidence of post-incubation or the revisit to a previously unsolved …

P2v-Rcnn: Point To Voxel Feature Learning For 3d Object Detection From Point Clouds, Jiale Li, Yu Sun, Shujie Luo, Ziqi Zhu, Hang Dai, Andrey S. Krylov, Yong Ding, Ling Shao

Computer Vision Faculty Publications

The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, the detection accuracy is severely restricted by the information loss of pose details in the voxels. Different from considering the point cloud as voxel or point representation only, we propose a point-to-voxel feature learning approach to voxelize the point cloud with both the point-wise semantic and local spatial features, which maintains the voxel-wise features to build the high-recall voxel-based RPN and also provides the accurate …

Caries And Restoration Detection Using Bitewing Film Based On Transfer Learning With Cnns, Yi-Cheng Mao, Tsung-Yi Chen, He-Sheng Jhou, Szu-Yin Lin, Sheng-Yu Liu, Yu-An Chen, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Chun-Wei Li, Patricia Angela R. Abu, Wei-Yuan Chiang

Department of Information Systems & Computer Science Faculty Publications

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of …

Facility Location Games With Ordinal Preferences, Hau Chan, Minming Li, Chenhao Wang

School of Computing: Faculty Publications

We consider a new setting of facility location games with ordinal preferences. In such a setting, we have a set of agents and a set of facilities. Each agent is located on a line and has an ordinal preference over the facilities. Our goal is to design strategyproof mechanisms that elicit truthful information (preferences and/or locations) from the agents and locate the facilities to minimize both maximum and total cost objectives as well as to maximize both minimum and total utility objectives. For the four possible objectives, we consider the 2-facility settings in which only preferences are private, or locations …

Understanding User's Behavior And Protection Strategy Upon Losing, Or Identifying Unauthorized Access To Online Account, Huzeyfe Kocabas, Swapnil Nandy, Tanjina Tamanna, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

A wide-range of personal and sensitive information are stored in users’ online accounts. Losing access, or an unauthorized access to one of those accounts could put them into the risks of privacy breach, cause financial loss, and compromise their accessibility to important information and documents. A large body of prior work focused on developing new schemes and strategies to protect users’ online security. However, there is a dearth in existing literature to understand users’ strategies and contingency plans to protect their online accounts once they lose access, or identify an unauthorized access to one of their accounts. We addressed this …

Understanding User Behavior, Information Exposure, And Privacy Risks In Managing Old Devices, Mahdi Nasrullah Al-Ameen, Tanjina Tamanna, Swapnil Nandy, Huzeyfe Kocabas

Computer Science Student Research

The goal of this study is to understand the behavior of users from developing countries in managing an old device (e.g., computer, mobile phone), which has been replaced by a new device, or suffers from technical issues providing a notion that it may stop working soon. The prior work explored the ecology and challenges of repairing old devices in developing regions. However, it is still understudied how the strategies of people from developing countries in managing their personal information on old devices could impact their digital privacy. To address this gap in existing literature, we conducted semi-structured interview with 52 …

Design, Deployment, And Validation Of Computer Vision Techniques For Societal Scale Applications, Arup Kanti Dey

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence techniques have ensued a significant impact on our daily lives. Numerous applications in so many diverse fields have been made possible by AI algorithms today, and there are many more yet to come. In this dissertation, we design, deploy and validate computer vision algorithms for innovative and high-impact societal scale applications.We specifically focus on two applications in this dissertation: Detection of distracted driving and Detection of breeding habitats of mosquito vectors.

Distracted driving on roads is a major problem around the world. Distracted driving is the case where a driver diverts his/her focus from the road and engages …

Reshaping The Landscape Of The Future: Software-Defined Manufacturing, Lei Xu, Lin Chen, Zhimin Gao, Hiram Moya, Weidong Shi

Computer Science Faculty Publications

We describe the concept of software-defined manufacturing, which divides the manufacturing ecosystem into software definition and physical manufacturing layers. Software-defined manufacturing allows better resource sharing and collaboration, and it has the potential to transform the existing manufacturing sector.

Infinite Peirce Distribution In The Algebra Of Compact Operators And Description Of Its Local Au-Tomorphisms, Farhodjon N. Arzikulov, Rejabboy Qo’Shaqov

Scientific Bulletin. Physical and Mathematical Research

In the present paper the infinite Peirce decomposition of the algebra 𝐾(𝐻) of com-pact operators on an infinite dimensional separable Gilbert space 𝐻 is constructed, using the norm of the algebra 𝐾(𝐻) and a maximal family of mutually or-thogonal minimal projections, i.e., self-adjoint,idempotent elements. The infinite Peirce decompo-sition on the norm of a 𝐶∗-algebra is also con-structed in 2012 by the first author. But, it turns, the condition, applied then, is not sufficient for the infi-nite Peirce decomposition on the norm constructed in 2012 to be an algebra. Therefore, in the present paper, the infinite Peirce decomposition on the norm …

What Is The Uncertainty Of The Result Of Data Processing: Fuzzy Analogue Of The Central Limit Theorem, Julio C. Urenda, Olga Kosheleva, Shahnaz Shahbazova, Vladik Kreinovich

Departmental Technical Reports (CS)

It is known that, due to the Central Limit Theorem, the probability distribution of the uncertainty of the result of data processing is, in general, close to Gaussian -- or to a distribution from a somewhat more general class known as infinitely divisible. We show that a similar result holds in the fuzzy case: namely, the membership function describing the uncertainty of the result of data processing is, in general, close to Gaussian -- or to a membership function from an explicitly described more general class.

Certis: Digital Transformation Of A Physical Security Company, Singapore Management University

Perspectives@SMU

How a company synonymous with auxiliary police injected sensors and artificial intelligence into its 21st century operations

On The Use Of Minimum Penalties In Statistical Learning, Ben Sherwood, Bradley S. Price

Faculty & Staff Scholarship

Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not scale to other types of models. In this article we proposed the MinPEN framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using mild assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to exploit detected relationships between responses. An iterative algorithm that …

Edge Detail Analysis Of Wear Particles, Mohammad Shakeel Laghari, Ahmed Hassan, Mubashir Noman

Computer Vision Faculty Publications

Tribology is the study of wear particles that are generated in all machines with interacting mechanical parts. Particles are separated from the surfaces due to friction and relative motion. These microscopic particles vary in certain characteristics of size, quantity, composition, and morphology. Wear particles or wear debris are categorized by six morphological attributes of shape, edge details, texture, color, size, and thickness ratio. Particles can be identified with the help of some or all of these attributes however, only edge details analysis is considered in this paper. The objective is to classify these particles in a coherent way based on …