Computer Engineering Commons^™

A Comparison Of Robotic Hand Thumb Designs, Ryon Miro

Journal of the South Carolina Academy of Science

No abstract provided.

Data Scarcity In Event Analysis And Abusive Language Detection, Sheikh Muhammad Sarwar

Doctoral Dissertations

Lack of data is almost always the cause of the suboptimal performance of neural networks. Even though data scarce scenarios can be simulated for any task by assuming limited access to training data, we study two problem areas where data scarcity is a practical challenge: event analysis and abusive content detection} Journalists, social scientists and political scientists need to retrieve and analyze event mentions in unstructured text to compute useful statistical information to understand society. We claim that it is hard to specify information need about events using keyword-based representation and propose a Query by Example (QBE) setting for event …

Searching For Oa Scholarly Content, Olga Koz

All Things Open

Academic search engines have become the number one resource to find scholarly resources. In contrast, search engines of academic databases, like Web of Science and Scopus, harvest research which is locked behind paywalls. Google Scholar and other academic search engines assist in finding open access content as well as the content of commercial databases. Dr. Olga Koz, Senior Research Support Librarian, will present academic search engines that enhance expert research on various academic subject matters.

Tutorial: Knowledge-Infused Learning For Autonomous Driving (Kl4ad), Ruwan Wickramarachchi, Cory Henson, Sebastian Monka, Daria Stepanova, Amit Sheth

Publications

Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and …

Adaptive Multi-Scale Place Cell Representations And Replay For Spatial Navigation And Learning In Autonomous Robots, Pablo Scleidorovich

USF Tampa Graduate Theses and Dissertations

Place cells are one of the most widely studied neurons thought to play a vital role in spatial cognition. Extensive studies show that their activity in the rodent hippocampus is highly correlated with the animal’s spatial location, forming “place fields” of smaller sizes near the dorsal pole and larger sizes near the ventral pole. Despite advances, it is yet unclear how this multi-scale representation enables navigation in complex environments.

In this dissertation, we analyze the place cell representation from a computational point of view, evaluating how multi-scale place fields impact navigation in large and cluttered environments. The objectives are to …

Research On The Early Warning And Intervention Of Learning Crisis Based On Smart Classroom, Tan Aiping, Wang Sainan

International Journal of Computer and Communication Technology

Under the normal state of online and offline integrated learning of open courses, the low participation of learners and low learning results are hot issues that scholars in the industry pay more attention to. Accurate learning crisis warning and personalized teaching intervention are important measures to solve the above problems and improve teaching quality. Based on the analysis of the shortcomings of the existing learning early warning and teaching intervention, this study constructs a research framework of online open course learning early warning and intervention under the intelligent classroom learning environment. The framework diagnoses and warns learners' learning state from …

Design Of An Efficient Memristor-Based Dynamic Exclusive-Or Gate., Annu Chauhan, Dishika Chopra, Lirisha Tayal, Utsav Singal, Kirti Gupta, Monica Gupta

International Journal of Computer and Communication Technology

In this paper, an efficient memristor-based dynamic logic design for an Exclusive-OR gate is proposed. The proposed realization reduces the number of cascaded stages and component count thereby providing an overall performance improvement. The performance of the proposed design is compared with the most recent existing design through LTspice software simulations at 32 nm technology node in terms of total power consumption, average propagation delay, and number of components used in the implementation. The outcomes depict that the proposed design consumes 57 % reduced power and provides faster operation with 5.09 % improvement in propagation delay in comparison to its …

Updated Perspectives On The Role Of Biomechanics In Copd: Considerations For The Clinician, Jennifer M. Yentes, Wai-Yan Liu, Kuan Zhang, Eric J. Markvicka, Stephen I. Rennard

Faculty Publications from the Department of Electrical and Computer Engineering

Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline …

Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …

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 …

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, …

Rural Broadband Usage: Analyzing Satisfaction And Internet Speed Within The Rural Digital Divide, Angela Hollman, Tim Obermier, Jesse Andrews

Mountain Plains Business Conference

No abstract provided.

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 …

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 …

Enginews Fall 2022, School Of Computer Science & Engineering

News, Magazines and Reports

In this issue:

• New Engineering professor, Okechukwu ‘Okey’ Ugweje
• Business Minor
• S-STEM Grant, National Science Foundation
• Embedded Systems course
• Engineering Explorations course
• SHU Innovate club
• Formula SAE Go-Kart "Road Kill"
• Recent faculty publications and press releases

Columnas: The Honors Program Newsletter At Bentley University, Debayan Sen, Hailey Jennato, Gabe Holmes, Daniel Furze

Honors Program

Page 1: SOCIAL MEDIA—A VEHICLE FOR SOCIAL CHANGE OR VIRTUE SIGNALING? ~ By Debayan Sen ’23

Page 2: WILL ARTIFICIAL INTELLIGENCE AND ROBOTICS REPLACE THE HUMAN WORKER? ~ By Hailey Jennato ’24

Page 3: HOW TO HEALTHILY COMMUNICATE IN A RELATIONSHIP: NO, NOT JUST A ROMANTIC ONE ~ By Gabe Holmes ’26

Page 4: THE W SLANT ~ By Daniel Furze ’26

Reflecting On Experiences For Response Generation, Chenchen Ye, Lizi Liao, Suyu Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal dialogue systems attract much attention recently, but they are far from skills like: 1) automatically generate context- specific responses instead of safe but general responses; 2) naturally coordinate between the different information modalities (e.g. text and image) in responses; 3) intuitively explain the reasons for generated responses and improve a specific response without re-training the whole model. To approach these goals, we propose a different angle for the task - Reflecting Experiences for Response Generation (RERG). This is supported by the fact that generating a response from scratch can be hard, but much easier if we can access other …

Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Weerakoon Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Minh Anh Tuan Tran, Archan Misra

Research Collection School Of Computing and Information Systems

Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual …

Remgen: Remanufacturing A Random Program Generator For Compiler Testing, Haoxin Tu, He Jiang, Xiaochen Li, Zhide Zhou, Lingxiao Jiang, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Program generators play a critical role in generating bug-revealing test programs for compiler testing. However, existing program generators have been tamed nowadays (i.e., compilers have been hardened against test programs generated by them), thus calling for new solutions to improve their capability in generating bug-revealing test programs. In this study, we propose a framework named Remgen, aiming to Remanufacture a random program Generator for this purpose. RemgEnaddresses the challenges of the synthesis of diverse code snippets at a low cost and the selection of the bug-revealing code snippets for constructing new test programs. More specifically, RemgEnfirst designs a grammar-aided synthesis …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …