Computer Sciences Commons^™

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of …

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Fusion Of 3d Lidar And Camera Data For Object Detection In Autonomous Vehicle Applications, Xiangmo Zhao, Pengpeng Sun, Zhigang Xu, Haigen Min, Hongkai Yu

Computer Science Faculty Publications

It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects in its vicinity, which will fully guarantee the safety of the passengers and vehicle in various environment. 3D LIDAR can directly obtain the position and geometrical structure of the object within its detection range, while vision camera is very suitable for object recognition. Accordingly, this paper presents a novel object detection and identification method fusing the complementary information of two kind of sensors. We first utilize the 3D LIDAR data to generate accurate object-region proposals effectively. Then, these candidates are mapped into the image space …

Smart Green Communication Protocols Based On Several-Fold Messages Extracted From Common Sequential Patterns In Uavs, Iván García-Magariño, Geraldine Gray, Raquel Lacuesta, Jaime Lloret

Articles

Green communications can be crucial for saving energy in UAVs and enhancing their autonomy. The current work proposes to extract common sequential patterns of communications to gather each common pattern into a single several- fold message with a high-level compression. Since the messages of a pattern are elapsed from each other in time, the current approach performs a machine learning approach for estimating the elapsed times using off-line training. The learned predictive model is applied by each UAV during flight when receiving a several-fold compressed message. We have explored neural networks, linear regression and correlation analyses among others. The current …

Coordinated Container Migration And Base Station Handover In Mobile Edge Computing, Mao V. Ngo, Tie Luo, Hieu T. Hoang, Q. S. Tony Quek

Computer Science Faculty Research & Creative Works

Offloading computationally intensive tasks from mobile users (MUs) to a virtualized environment such as containers on a nearby edge server, can significantly reduce processing time and hence end-to-end (E2E) delay. However, when users are mobile, such containers need to be migrated to other edge servers located closer to the MUs to keep the E2E delay low. Meanwhile, the mobility of MUs necessitates handover among base stations in order to keep the wireless connections between MUs and base stations uninterrupted. In this paper, we address the joint problem of container migration and base-station handover by proposing a coordinated migration-handover mechanism, with …

On The Validation Of Web X.509 Certificates By Tls Interception Products, Ahmad Samer Wazan, Romain Laborde, David Chadwick, Remi Venant, Abdelmalek Benzekri, Eddie Billoir, Omar Alfandi

All Works

The Transport Layer Security (TLS) protocol aims to provide confidentiality and integrity of data. It is based on X.509 Certificates. Our previous research showed that popular Web Browsers exhibit non-standardized behaviour with respect to the certificate validation process [1]. This paper extends that work by examining their handling of OCSP Stapling. We also examine several popular HTTPS interception products, including proxies and anti-virus tools, regarding their certificate validation processes. We analyse and compare their behaviour to that described in the relative standards. Finally, we propose a system that allows the automation of certificate validation tests.

Hierarchical Shape Construction And Complexity For Slidable Polyominoes Under Uniform External Forces, Jose Balanza-Martinez, David Caballero, Angel A. Cantu, Mauricio Flores, Timothy Gomez, Austin Luchsinger, Rene Reyes, Robert Schweller, Tim Wylie

Computer Science Faculty Publications

Advances in technology have given us the ability to create and manipulate robots for numerous applications at the molecular scale. At this size, fabrication tool limitations motivate the use of simple robots. The individual control of these simple objects can be infeasible. We investigate a model of robot motion planning, based on global external signals, known as the tilt model. Given a board and initial placement of polyominoes, the board may be tilted in any of the 4 cardinal directions, causing all slidable polyominoes to move maximally in the specified direction until blocked.

We propose a new hierarchy of shapes …

Multimodal Fusion Strategies For Outcome Prediction In Stroke, Esra Zihni, John D. Kelleher, Vince I. Madai, Ahmed Khalil, Ivana Galinovic, Jochen Fiebach, Michelle Livne, Dietmar Frey

Conference papers

Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental …

Language Model Co-Occurrence Linking For Interleaved Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

Novice Learner Experiences In Software Development: A Study Of Freshman Undergraduates, Catherine Higgins, Ciaran O'Leary, Claire Mcavinia, Barry J. Ryan

Conference papers

This paper presents a study that is part of a larger research project aimed at addressing the gap in the provision of educational software development processes for freshman, novice undergraduate learners, to improve proficiency levels. With the aim of understanding how such learners problem solve in software development in the absence of a formal process, this case study examines the experiences and depth of learning acquired by a sample set of novice undergraduates. A novel adaption of the Kirkpatrick framework known as AKM-SOLO is used to frame the evaluation. The study finds that without the scaffolding of an appropriate structured …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Explainable Artificial Intelligence: Concepts, Applications, Research Challenges And Visions, Luca Longo, Randy Goebel, Freddy Lecue, Peter Kieseberg, Andreas Holzinger

Conference papers

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like …

Design And Evaluation Of An Adventure Videogame Based In The History Of Mathematics, Mariana Rocha, Pierpaolo Dondio

Conference papers

The present paper describes the design and evaluation of an adventure videogame developed to cover the mathematics primary school curriculum. The narrative of the game is based in the history of mathematics and, to win, the player needs to travel through time, starting from the ancient Egypt and finishing at the modern world. To achieve that, the player interacts with real-life characters, such as Pythagoras of Samos, learning about their contributions to the field and using this knowledge to solve puzzles. The aim of the research presented in this paper is to understand the effects of the game on students’ …

Smpl-Based 3d Pedestrian Pose Prediction, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche, Lorraine D'Arcy

Conference papers

Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently …

Brexit: Psychometric Profiling The Political Salubrious Through Machine Learning: Predicting Personality Traits Of Boris Johnson Through Twitter Political Text, James Usher, Pierpaolo Dondio

Conference papers

Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that people's psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnson’s Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN …

Comparing Tagging Suggestion Models On Discrete Corpora, Bojan Bozic, Andre Rios, Sarah Jane Delany

Articles

This paper aims to investigate the methods for the prediction of tags on a textual corpus that describes diverse data sets based on short messages; as an example, the authors demonstrate the usage of methods based on hotel staff inputs in a ticketing system as well as the publicly available StackOverflow corpus. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry.

Importance Of Data Distribution On Hive-Based Systems For Query Performance: An Experimental Study, Hilmi Egemen Ciritoglu, John Murphy, Christina Thorpe

Articles

SQL-on-Hadoop systems have been gaining popularity in recent years. One popular example of SQL-on-Hadoop systems is Apache Hive; the pioneer of SQL-on-Hadoop systems. Hive is located on the top of big data stack as an application layer. Besides the application layer, the Hadoop Ecosystem is composed of 3 different main layers: storage, the resource manager and processing engine. The demand from industry has led to the development of new efficient components for each layer. As the ecosystem evolves over time, Hive employed different execution engines too. Understanding the strengths of components is very important in order to exploit the full …

Analysis Of Automatic Annotations Of Real Video Surveillance Images, Diana Guevara Flores, Fernando Pérez Téllez, David Pinto Avendaño

Articles

The results of the analysis of the automatic annotations of real video surveillance sequences are presented. The annotations of the frames of surveillance sequences of the parking lot of a university campus are generated. The purpose of the analysis is to evaluate the quality of the descriptions and analyze the correspondence between the semantic content of the images and the corresponding annotation. To perform the tests, a fixed camera was placed in the campus parking lot and video sequences of about 20 minutes were obtained, later each frame was annotated individually and a text repository with all the annotations was …

An Univariable Approach For Forecasting Workload In The Maintenance Industry, Paulo Silva, Fernando Pérez Téllez, John Cardiff

Articles

The forecasting of the workload in the maintenance industry is of great value to improve human resources allocation and reduce overwork. In this paper, we discuss the problem and the challenges it pertains. We analyze data from a company operating in the industry and present the results of several forecasting models.

Tmvis: Visualizing Webpage Changes Over Time, Abigail Mabe, Dhruv Patel, Maheedhar Gunnam, Surbhi Shankar, Mat Kelly, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

TMVis is a web service to provide visualizations of how individual webpages have changed over time. We leverage past research on summarizing collections of webpages with thumbnail-sized screenshots and on choosing a small number of representative archived webpages from a large collection. We offer four visualizations: Image Grid, Image Slider, Timeline, and Animated GIF. Embed codes for the Image Grid and Image Slider can be produced to include these visualizations on separate webpages. This tool can be used to allow scholars from various disciplines, as well as the general public, to explore the temporal nature of webpages.

Heterogeneous Multi-Layered Network Model For Omics Data Integration And Analysis, Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, Lei Xie

Faculty Work

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful …

The Ai Author In Litigation, Yvette Joy Liebesman, Julie Cromer Young

All Faculty Scholarship

Many scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.

However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on …

Accessibility Of Deepfakes, Andrew L. Collings

Cybersecurity Undergraduate Research Showcase

The danger posed by falsified media, commonly referred to as deepfakes, has been well researched and documented. The software Faceswap to was used to swap the faces of two politician (Joe Biden and Donald Trump). The testing was performed using an affordable consumer GPU (an AMD Radeon RX 570) over 100,000 iterations. The process and results for the two attempts with the best results (and largest differences) were recorded. The result was ultimately unconvincing, while the software was able to recreate the facial structure the lighting and skin tone did not blend at all.

The Interdisciplinary Impacts Of Technology Semantics And Communicational Bypassing In The Cybersecurity Field, Brooke Nixon

Cybersecurity Undergraduate Research Showcase

In 2012, former director of the Federal Bureau of Investigation (FBI) Robert Mueller shocked many when he warned, “[t]here are only two types of companies: those that have been hacked and those that will be.” As technology and information systems develop rapidly, security has become a topic of increasing interest and concern. In 2018, it was noted that the total cost to account for cybercrime on a global scale surpassed US$1 billion (Milkovich, 2020). In the United States, a hacker attack occurs every 39 seconds, and each year, 1 in 3 Americans are personally impacted by a form of hacking …

Outlier Profiles Of Atomic Structures Derived From X-Ray Crystallography And From Cryo-Electron Microscopy, Lin Chen, Jing He, Angelo Facchiano

Computer Science Faculty Publications

Background: As more protein atomic structures are determined from cryo-electron microscopy (cryo-EM) density maps, validation of such structures is an important task. Methods: We applied a histogram-based outlier score (HBOS) to six sets of cryo-EM atomic structures and five sets of X-ray atomic structures, including one derived from X-ray data with better than 1.5 Å resolution. Cryo-EM data sets contain structures released by December 2016 and those released between 2017 and 2019, derived from resolution ranges 0–4 Å and 4–6 Å respectively. Results: The distribution of HBOS values in five sets of X-ray structures show that HBOS is sensitive distinguishing …

Mementoembed And Raintale For Web Archive Storytelling, Shawn M. Jones, Martin Klein, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

For traditional library collections, archivists can select a representative sample from a collection and display it in a featured physical or digital library space. Web archive collections may consist of thousands of archived pages, or mementos. How should an archivist display this sample to drive visitors to their collection? Search engines and social media platforms often represent web pages as cards consisting of text snippets, titles, and images. Web storytelling is a popular method for grouping these cards in order to summarize a topic. Unfortunately, social media platforms are not archive-aware and fail to consistently create a good experience for …

Aligning Workforce Training Center Curricula With Local Business Needs, Elizabeth P. Burns

Walden Dissertations and Doctoral Studies

Many cities in the United States have experienced job loss due to a shift from industrial-based to information and service economies, as well as the outsourcing of labor jobs to overseas locations. In the absence of industrial jobs once occupied by low-skilled workers, the problem that compelled this study was a perceived gap between the skills required by the employers who now hire such workers and the actual skills those workers have. The purpose of this qualitative case study was to explore the espoused skills required by employers in a rural region of the southeastern United States to inform the …

Strategies For Managing Offshore Outsourcing For Business Sustainability, Idrissa Maiga Hamidou Issa

Walden Dissertations and Doctoral Studies

Ineffective offshore outsourcing strategies can contribute to unexpected and significant costs for businesses. Strategies to improve outsourcing outcomes are critical to business managers because often third parties contracted to implement offshore outsourcing fail to fulfil business managers’ expectations who can benefit from understanding how to manage outsourced operations and anticipate potential problems. Grounded in transaction cost theory, cultural and customs theory, and the resource-based view theory, the purpose of this qualitative single case study was to explore strategies and processes business managers use to maintain their organization after the implementation of offshore outsourcing. The participants consisted of 3 managers who …

Strategies For Reducing Adverse Medical Events From Implanted Medical Devices, Gary John Zack

Walden Dissertations and Doctoral Studies

Managing medical device monitoring processes is challenging and lacks a realtime, life cycle tracking strategy to reduce adverse medical events and revision costs for hospital administrators, physicians, and patients. Understanding the malfunctions of medical devices for cardiac and orthopedic patients could save lives and reduce hospital liability. Grounded in the business process reengineering conceptual framework, the purpose of this single qualitative case study was to explore strategies hospital managers used to redesign the implant recall surveillance process at one hospital in Pennsylvania. The 5 participants selected successfully implemented a medical device surveillance process that reduced adverse medical events and revision …