Computer Sciences Commons^™

Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid, Hanan Lutfiyya, Katarina Grolinger

Electrical and Computer Engineering Publications

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern …

Predicting Guiding Entities For Entity Aspect Linking, Shubham Chatterjee, Laura Dietz

Computer Science Faculty Research & Creative Works

Entity linking can disambiguate mentions of an entity in text. However, there are many different aspects of an entity that could be discussed but are not differentiable by entity links, for example, the entity "oyster" in the context of "food" or "ecosystems". Entity aspect linking provides such fine-grained explicit semantics for entity links by identifying the most relevant aspect of an entity in the given context. We propose a novel entity aspect linking approach that outperforms several neural and non-neural baselines on a large-scale entity aspect linking test collection. Our approach uses a supervised neural entity ranking system to predict …

Towards High Performing And Reliable Deep Convolutional Neural Network Models For Typically Limited Medical Imaging Datasets, Kaoutar Ben Ahmed

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence has been applied in a wide range of fields including automobiles, space, robotics, and healthcare.

According to recent reports, AI will have a huge impact on increasing the world economy by 2030 and it's expected that the greatest impact will be in the field of healthcare. The global market size of AI in healthcare was estimated at USD 10.4 billion in 2021 and is expected to grow at a high rate from 2022 to 2030 (CAGR of 38.4%). Applications of AI in …

Pausing While Programming: Insights From Keystroke Analysis, Raj Shrestha, Juho Leinonen, Albina Zavgorodniaia, Arto Hellas, John M. Edwards

Computer Science Student Research

Pauses in typing are generally considered to indicate cognitive processing and so are of interest in educational contexts. While much prior work has looked at typing behavior of Computer Science students, this paper presents results of a study specifically on the pausing behavior of students in Introductory Computer Programming. We investigate the frequency of pauses of different lengths, what last actions students take before pausing, and whether there is a correlation between pause length and performance in the course. We find evidence that frequency of pauses of all lengths is negatively correlated with performance, and that, while some keystrokes initiate …

Ideating Xai: An Exploration Of User’S Mental Models Of An Ai-Driven Recruitment System Using A Design Thinking Approach, Helen Sheridan, Dympna O'Sullivan, Emma Murphy

Conference Papers

Artificial Intelligence (AI) is playing an important role in society including how vital, often life changing decisions are made. For this reason, interest in Explainable Artificial Intelligence (XAI) has grown in recent years as a means of revealing the processes and operations contained within what is often described as a black box, an often-opaque system whose decisions are difficult to understand by the end user. This paper presents the results of a design thinking workshop with 20 participants (computer science and graphic design students) where we sought to investigate users' mental models when interacting with AI systems. Using two personas, …

Educational Equivalency Of Raspberry Pi Clusters In High-Performance Computing, Victoria A. Steckline, Robert Houghton

Mountain Plains Business Conference

Abstract

High-performance computing is a difficult subject to teach in an academic setting, given the exorbitant costs and technical difficulties. Raspberry Pi single-board computers have been used in recent years to create clusters that function as mini high-performance computers. The purpose of this research is to evaluate the educational equivalence of building a Raspberry Pi cluster in comparison to running a high-performance computing environment. For this research, an eight-node cluster was built and tested in comparison to a laptop. Through the process of building the cluster, skills learned were documented to evaluate the educational value. This research concludes that the …

An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms, Anastasia Natsiou, Luca Longo, Seán O'Leary

Conference Papers

In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Supporting Prosocial Behaviour In Online Communities Through Social Media Affordances, Dominique Kelly, Liu Yifan, Alex Mayhew, Sarah Cornwell, Yimin Chen, Nicole Delellis, Victoria Rubin

Data and Test Instruments

Affordances are action possibilities that emerge from the relationship between the properties of an object and an interacting agent’s capabilities. This poster examines how one type of affordance—anonymity—is enabled or constrained by six features of social media platforms. Our work is one step in a broader agenda to: (1) identify affordances that influence users’ behaviour in online communities; (2) outline the social media features that enable or constrain those affordances; and, (3) experimentally determine whether certain affordances, or combinations of affordances, support prosocial behaviour. Prosocial behaviour in information and communication technologies (ICTs) is broadly viewed here as benefiting other individuals …

Xai Analysis Of Online Activism To Capture Integration In Irish Society Through Twitter, Arjumand Younus, Muhammad Atif Qureshi, Mingyeong Jeon, Arefeh Kazemi, Simon Caton

Books/Book Chapters

Online activism over Twitter has assumed a multidimensional nature, especially in societies with abundant multicultural identities. In this paper, we pursue a case study of Ireland’s Twitter landscape and specifically migrant and native activists on this platform. We aim to capture the level to which immigrants are integrated into Irish society and study the similarities and differences between their characteristic patterns by delving into the features that play a significant role in classifying a Twitterer as a migrant or a native. A study such as ours can provide a window into the level of integration and harmony in society.

A Bilevel Optimization Model Based On Edge Computing For Microgrid, Yi Chen, Kadhim Hayawi, Meikai Fan, Shih Yu Chang, Jie Tang, Ling Yang, Rui Zhao, Zhongqi Mao, Hong Wen

All Works

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control …

Visual Object Tracking With Discriminative Filters And Siamese Networks: A Survey And Outlook, Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas

Computer Vision Faculty Publications

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in …

Software Protection And Secure Authentication For Autonomous Vehicular Cloud Computing, Muhammad Hataba

Dissertations

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.

In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our …

Deep Learning Methods For Malware And Intrusion Detection: A Systematic Literature Review, Rahman Ali, Asmat Ali, Farkhund Iqbal, Mohammed Hussain, Farhan Ullah

All Works

Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we …

Principles Of Information Security, Alison Hedrick

KSU Distinguished Course Repository

An introduction to the various technical and administrative aspects of Information Security and Assurance. This course provides the foundation for understanding the key issues associated with protecting information assets, determining the levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features.

Improving Protein Succinylation Sites Prediction Using Embeddings From Protein Language Model, Suresh Pokharel, Pawel Pratyush, Michael Heinzinger, Robert H. Newman, Dukka Kc

Michigan Tech Publications, Part 1

Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 …

Ps-Arm: An End-To-End Attention-Aware Relation Mixer Network For Person Search, Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan, Rao Anwer, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, previous study focuses on rich feature information learning, it’s still hard to retrieve the query person due to the occurrence of appearance deformations and background distractors. In this paper, we propose a novel attention-aware relation mixer (ARM) module for person search, which exploits the global relation between different local regions within RoI of a person and make it robust against various appearance deformations and occlusion. The proposed ARM is composed of a relation …

Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael

Publications and Research

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …

Deep Learning-Based Segmentation And Classification Of Leaf Images For Detection Of Tomato Plant Disease, Muhammad Shoaib, Tariq Hussain, Babar Shah, Ihsan Ullah, Sayyed Mudassar Shah, Farman Ali, Sang Hyun Park

All Works

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a …

Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti

Engineering Faculty Articles and Research

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the …

Emotion-Aware Music Recommendation, Hieu Tran, Tuan Le, Anh Do, Tram Vu, Steven Bogaerts, Brian T. Howard

Annual Student Research Poster Session

People often listen to songs that match their mood. Thus, an AI music recommendation system that is aware of the user’s emotions is likely to provide a superior user experience to one that is unaware. In this paper, we present an emotion-aware music recommendation system. Multiple models are discussed and evaluated for affect identification from a live image of the user. We propose two models: DRViT, which applies dynamic routing to vision transformers, and InvNet50, which uses involution. All considered models are trained and evaluated on the AffectNet dataset. Each model outputs the user’s estimated valence and arousal under the …

Diagnosing The Present With An Ecotopian Lexicon, Chirag Giri, Adam Liebman

Annual Student Research Poster Session

The book An Ecotopian Lexicon (2019) presents a collection of thirty terms and concepts from speculative fiction, anthropology, and the sociology of subcultures, each explored by a different author. The lexicon intends to address our collective “poverty of imagination” when it comes to avoiding global environmental collapse and building a different world, evidenced by the increasing dominance of apocalyptic narratives in popular culture. Although the book’s primary aim is to explore diverse concepts for imagining better futures, we have analyzed the form and content of the book for what they tell us about the present.

Sd Prisms 2d & 3d: Visualizing The Standard Deviation, Hieu Nguyen, Tom Nguyen, Mamunur Rashid, Jyotirmoy Sarkar

Annual Student Research Poster Session

SD Prism is a graphical R package used for visualizing the standard deviation of a data set. Given a raw data set, the standard deviation (SD) is defined as the square root of the sample variance. Sarkar and Rashid (2017) interpret the sample SD as the square root of twice the mean square of all pairwise half deviations between any two sample observations. This interpretation leads to a geometric visualization of the sample SD and a more elementary explanation as to why the denominator in the sample variance is one less than the sample size. In this article, we will …

Hyperfast Second-Order Local Solvers For Efficient Statistically Preconditioned Distributed Optimization, Pavel Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich, Soomin Lee, Erik Ordentlich, César A. Uribe, Alexander Gasnikov

Machine Learning Faculty Publications

Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in parallel, which are then used by the central node to update the parameter by solving an auxiliary (preconditioned) smaller-scale optimization problem. The recently proposed Statistically Preconditioned Accelerated Gradient (SPAG) method [1] has complexity bounds superior to other such algorithms but requires an exact solution for computationally intensive auxiliary optimization problems at every iteration. In this paper, we propose an Inexact SPAG (InSPAG) and explicitly characterize the accuracy by which the corresponding auxiliary subproblem needs to be solved to guarantee …

Multicriteria Decision Making For Carbon Dioxide (Co2) Emission Reduction, Rahman Ali, Farkhund Iqbal, Muhammad Sadiq Hassan Zada

All Works

The fast industrial revolution all over the world has increased emission of carbon dioxide (CO2), which has badly affected the atmosphere. Main sources of CO2 emission include vehicles and factories, which use oil, gas, and coal. Similarly, due to the increased mobility of automobiles, CO2 emission increases day-by-day. Roughly, 40% of the world’s total CO2 emission is due to the use of personal cars on busy and congested roads, which burn more fuel. In addition to this, the unavailability of parking in all parts of the cities and the use of conventional methods for searching parking areas have added more …

Location Verification For Future Wireless Vehicular Networks: Research Directions And Challenges, Shihao Yan, Ullah Ihsan, Robert Malaney, Linlin Sun, Stefano Tomasin

Research outputs 2022 to 2026

Vehicle location information obtained through the global navigation satellite system (GNSS) will play a pivotal role in emerging vehicular networks. This vital information is, however, susceptible to a host of unwanted manipulations, especially if a malicious entity is involved. The most obvious example of such manipulations is the forwarding by a malicious vehicle of false GNSS locations to other members of the network. Such events can lead to poor operational outcomes for the vehicular network, and in extreme cases even lead to catastrophic safety violations. Here, we highlight research efforts pursued in the past few years that have attempted to …

A Survey On Multimodal Disinformation Detection, Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimitar Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov

Natural Language Processing Faculty Publications

Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused …

Noisy Label Regularisation For Textual Regression, Yuxia Wang, Timothy Baldwin, Karin Verspoor

Natural Language Processing Faculty Publications

Training with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains. Our experiments suggest that standard regularisation strategies, such as weight decay and dropout, are ineffective in the face of noisy labels. We propose a simple noisy label detection method that prevents error propagation from the input layer. The approach is based on the observation that the projection of noisy labels is learned through memorisation at advanced stages of learning, and that the Pearson correlation is sensitive to outliers. Extensive experiments over real-world human-disagreement annotations as well as randomly-corrupted …

Lemurs Optimizer: A New Metaheuristic Algorithm For Global Optimization, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Iyad Abu Doush, Ashraf Elnagar, Eman H. Alkhammash, Myriam Hadjouni

Machine Learning Faculty Publications

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm …

Why 1/(1+D) Is An Effective Distance-Based Similarity Measure: Two Explanations, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Most of our decisions are based on the notion of similarity: we use a decision that helped in similar situations. From this viewpoint, it is important to have, for each pair of situations or objects, a numerical value describing similarity between them. This is called a similarity measure. In some cases, the only information that we can use to estimate the similarity value is some natural distance measure d(a,b). In many such situations, empirical data shows that the similarity measure 1/(1+d) is very effective. In this paper, we provide two explanations for this effectiveness.