Computer Sciences Commons^™

Research On Decision-Making Of Closed-Loop Supply Chain For Dual-Channel Recovery Based On Game Theory, Ying Xu, Qinming Liu, Linsen Zhou

Journal of System Simulation

Abstract: To tackle the difficulties and resource depletion in current packaging recycling, this paper constructs a centralized decision-making game model and three Stackelberg game models. Specifically, these Stackelberg game models are developed depending on the differences in the game power of participants in the closed-loop supply chain for dual-channel recovery, respectively corresponding to the cases where the manufacturer, the distributor or the third-party recycler is dominant. The optimal solutions of the four models are compared and analyzed. The benefits of decentralized decision-making do not reach the Pareto optimality as compared with centralized decision-making. An improved revenue sharing contract is …

Component Design And Simulation Of Netted Radar Fusion Processing, Jing Wu, Zhiming Xu, Xiaofeng Ai, Feng Zhao, Shunping Xiao

Journal of System Simulation

Abstract: Data fusion processing technology is the core of netted radars. Taking the air-defense radar network as the reference, this paper builds a component-based and reconfigurable data fusion algorithm library. With the component design method, the process of data fusion is divided into different components, such as data validity check, error match, time-space match, plot association, plot fusion, track initiation, track filtering, track association, track fusion, and track management. Each component involves different algorithms with a unified external interface, and algorithms can be chosen by parameter setting to meet different fusion requirements. Then, the complete processing template forplot fusion and …

A Practical Model Of Student Engagement While Programming, John M. Edwards, Kaden Hart, Christopher M. Warren

Computer Science Faculty and Staff Publications

We consider the question of how to predict whether a student is on or off task while working on a computer programming assignment using elapsed time since the last keystroke as the single independent variable. In this paper we report results of an empirical study in which we intermittently prompted CS1 students working on a programming assignment to self-report whether they were engaged in the assignment at that moment. Our regression model derived from the results of the study shows power-law decay in the engagement rate of students with increasing time of keyboard inactivity ranging from a nearly 80% engagement …

Iseeq: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval And Knowledge Graphs, Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin

Publications

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating …

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking For Unmanned Aerial Vehicle With Achievement Rewarding And Multistage Training, Najmaddin Abo Mosali, Syariful Syafiq Shamsudin, Omar Alfandi, Rosli Omar, Najib Al-Fadhali

All Works

Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It requires handling a high level of nonlinearity and dynamics. Model-free control effectively handles the uncertain nature of the problem, and reinforcement learning (RL)-based approaches are a good candidate for solving this problem. In this article, the Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3), as recent and composite architecture of RL, was explored as a tracking agent for the UAV-based target tracking problem. Several improvements on the original TD3 were also performed. First, the proportional-differential controller was used to boost the exploration of the TD3 in …

Leveraging Machine Learning For Detecting Iot-Based Interference In Operational Wifi Networks [Poster], Josh Pulse

Research in the Capitol

IoT (Internet of Things) devices have become increasingly popular in recent years. IoT includes many smart home devices such as an Amazon Echo, smart lightbulbs, and smart sensors. These devices often include different networking protocols than are used in most WiFi devices but are in the same wireless band, leading to the possibility of interference. With the rise in the number of IoT devices, it is important to understand how they impact the existing WiFi networks that many people deploy in their home or business. In this research project, wireless traffic data will be collected in an environment containing both …

Socioapp: Detecting Your Sociability Status With Your Smartphone, Aaron Walker

Research in the Capitol

Loneliness, isolation, and anti-social behaviors have increased in the past few years, whether that be due to social media, people paying more attention to their devices, or due to the COVID-19 pandemic. These behaviors are proven to decrease a student’s academic performance, causing their grades to decline, and disabling their motivation to learn. We aim to gain insight on this issue via the application of smartphone technology and machine learning, enabling those that use our app to understand if their being social or anti-social. We use a variety of sensors, location devices, and speaker recognition algorithms to identify behaviors that …

In-Vitro Evaluation Of High Dosage Of Curcumin Encapsulation In Palm-Oil-In-Water, Nanoemulsion Stabilized With A Sonochemical Approach, Uday Dasharath Bagale Phd, Aram Tsaturov, Irina Potoroko, Shital Potdar, Shirish Sonawane Phd

Karbala International Journal of Modern Science

Curcumin is unstable under different environmental conditions. To increase the bioavailability and stability of curcumin, it is proposed in the present study to encapsulate it in palm oil-in-water nanoemulsions using ultrasound. The present work deals with maximum curcumin encapsulation into oil forms stable nanoemulsion using a food-grade surfactant and examines its antioxidant assay and in-vitro study. The synthesized curcumin nanoemulsion (CuNE) shows particle size in the range of 14.7 ± 3 nm, 95 ± 0.5% encapsulation efficiency, and provides stability against different environmental parameters. Furthermore, in-vitro analysis of high dosage CuNE shows more than 80-90% retention during simulated gastric and …

Deep-Precognitive Diagnosis: Preventing Future Pandemics By Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Chao Cheng, Jing Zhang, Tianyang Wang, Min Xu

Computer Vision Faculty Publications

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them …

Thoughts On Data-Driven Discursive Logic And Triangulation Of Think Tank, Jianjun Sun, Lei Pei, Yaxue Ma, Yang Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rapid accumulation of data resources and the development of analysis technologies have expanded the scope of think tank research, and prompted think tanks to pay more attention to data intelligence. Meanwhile, higher requirements are put forward on the quality and innovation of the think tank. Facing the development needs of think tanks, i.e., modernization, innovation, and conscientization, this paper demonstrates the change of data-driven think tank researches from the perspective of the information chain. The paper analyzes the urgent need to reshape the discursive logic of the think tank, and discusses the construction scheme of triangulation for data-driven think …

Real-Time Complex Hand Gestures Recognition Based On Multi- Dimensional Features, Isack Bulugu

Tanzania Journal of Engineering and Technology (TJET)

Gesture recognition is broadly utilized within the field of sensing. There are basically three gesture recognition methods based on computer vision, depth sensor and motion sensor. Motion sensor-based gesture recognition has few input data, fast speed, and direct access to three- dimensional information of the hand. The advantages of traditional motion sensor-based gesture recognition have gradually become a current research hotspot. The essence of traditional motion sensor-based gesture recognition is a pattern recognition problem, and its accuracy depends heavily on the feature dataset extracted from prior experience. Unlike traditional pattern recognition methods, deep learning can be used to a large …

Complete Neighbourhood Search Heuristic Algorithm For Portfolio Optimization, Collether John

Tanzania Journal of Engineering and Technology (TJET)

In portfolio optimization, the fundamental goal of an investor is to optimally allocate investments between different assets. Mean-variance optimization methods make unrealistic assumptions to solve the problem of optimal allocation. On the other hand, when realistic constraints like holding size and cardinality are introduced it leads to optimal asset allocation which differ from the mean variance optimization. The resulting optimization problem become quite complex as it exhibits multiple local extrema and discontinuities. Heuristic algorithms work well for the complex problem. Therefore, a heuristic algorithm is developed which is based on hill climbing complete (HC-C). It is utilized to solve the …

Towards An Ethical Framework For The Design And Development Of Inclusive Home-Based Smart Technology For Smart Spaces For Older Adults And People With Disabilities, Emma Murphy, Julie Doyle, Ioannis Stavrakakis, Damian Gordon, Brian Keegan, Dympna O'Sullivan

Articles

Unique ethical, privacy and safety implications arise for people who are reliant on home-based smart technology due to health conditions or disabilities. As a result we need to carefully reflect on our approaches to ethical issues over the life cycle of smart home technology design and the wider living context for end users and relevant stakeholders. In this position paper we highlight a need for a reflective, inclusive ethical framework for the design of inclusive smart spaces. We present key ethical considerations in the design, development and deployment of smart home-based technology for older adults and people with disabilities. We …

Seizure Prediction In Epilepsy Patients, Gary Dean Cravens

NSU REACH and IPE Day

Purpose/Objective: Characterize rigorously the preictal period in epilepsy patients to improve the development of seizure prediction techniques. Background/Rationale: 30% of epilepsy patients are not well-controlled on medications and would benefit immensely from reliable seizure prediction. Methods/Methodology: Computational model consisting of in-silico Hodgkin-Huxley neurons arranged in a small-world topology using the Watts-Strogatz algorithm is used to generate synthetic electrocorticographic (ECoG) signals. ECoG data from 18 epilepsy patients is used to validate the model. Unsupervised machine learning is used with both patient and synthetic data to identify potential electrophysiologic biomarkers of the preictal period. Results/Findings: The model has shown states corresponding to …

Survey On Self-Supervised Representation Learning Using Image Transformations, Muhammad Ali, Sayed Hashim

Student Publications

Deep neural networks need huge amount of training data, while in real world there is a scarcity of data available for training purposes. To resolve these issues, self-supervised learning (SSL) methods are used. SSL using geometric transformations (GT) is a simple yet powerful technique used in unsupervised representation learning. Although multiple survey papers have reviewed SSL techniques, there is none that only focuses on those that use geometric transformations. Furthermore, such methods have not been covered in depth in papers where they are reviewed. Our motivation to present this work is that geometric transformations have shown to be powerful supervisory …

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew Whitinger

Tennessee Posters at the Capitol [2022]

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …

Stumbling Into Virtual Worlds. How Resolution Affects Users’ Immersion In Virtual Reality And Implications For Virtual Reality In Therapeutic Applications, Brianna Martinson

Tennessee Posters at the Capitol [2022]

Studies of how users experience Virtual Reality (VR) have thus far failed to address the extent to which rendering resolution and rendering frame rate affect users’ sense of immersion in VR, including applications of VR involving simulators, treatments for psychological and mental disorders, explorations of new and nonexistent structures, and ways to understand the human body in medical applications better. This study investigated if rendering resolution affected users’ sense of immersion in VR. The study compared the responses of two groups relative to two measures of participant immersion: (a) participant’s sense of presence and (b) participant’s sense of embodiment. The …

Android-Based Mobile App Design For Covid-19 Tracking, Paula Faidley

Tennessee Posters at the Capitol [2022]

With the evolution of smartphones and rise in their popularity, mobile applications have become a booming concept. Mobile applications connect users with things they value most. With the growing need of mobile applications, mobile app design is quickly becoming a sought-after skill. By learning the intricate process of mobile app design, individuals can learn new and innovative ways to express their creativity while providing useful products. During times such as the recent pandemic, mobile applications are especially useful because people would often times feel alone from spending a majority of the year in quarantine while being overwhelmed with all of …

Outvoice: Bringing Transparency To Healthcare, Autumn Clark

Undergraduate Honors Theses

Industries are not incentivized to price reasonably and spend responsibly if consumers do not have the ability to shop around within that industry, and shopping around is not possible without pricing transparency (knowing how much a good or service costs before purchasing it). But in the healthcare industry, we typically default to whichever clinic or hospital is closest, with no prior knowledge of what costs we can expect to incur at that particular institution. According to a poll published by Harvard University, nine out of ten Americans feel the healthcare industry is too opaque and greater transparency is needed.

We …

Rethinking Sampled-Data Control For Unmanned Aircraft Systems, Xinkai Zhang, Justin M. Bradley

School of Computing: Faculty Publications

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar …

Silver Nanoparticles Synthesized By Nd: Yag Laser Ablation Technique: Characterization And Antibacterial Activity, Sahar Naji Rashid Msc., Kadhim A. Aadim Ph.D., Awatif Sabir Jasim Ph.D.

Karbala International Journal of Modern Science

The pulsed laser ablation in liquid (PLAL) technique can produce high purity nanoparticles, it is a top-down physical method based on the principle of dividing metal ion bulk precursors into metal atoms, this method was used in this work to synthesize silver nanoparticles (AgPNs) by using Nd: YAG laser with two wavelengths (355 nm) and (532 nm) at energies (500 mJ) and (600 mJ) respectively, with the number of pulses (500, 600, 700, 800, and 900 pulses) for each wavelength. The properties of the prepared nanoparticles were investigated by UV-Vis, XRD, SEM with EDX, AFM, and FTIR analysis and then …

Simulation And Improvement Of The Efficiency Of The Cfts Solar Cell Using Scaps-1d, Hardan T. Ghanem Dr., Ayed N. Saleh, Muaamer A. Kamil

Karbala International Journal of Modern Science

A simulation of the AZO/i-ZnO/CdS/CFTS solar cell was carried out using SCAPS software. The results were obtained (Voc=0.55 V, Jsc=6.2 mA/cm², FF=39 %, ɳ=1.35 %). Simulation results were compared with experimental research and we got convergence in the results (Voc=0.56 V, Jsc=6.5 mA/cm², FF=37 %, ɳ=1.37 %). The simulated solar cell is improved by increasing the doping concentration and thickness of the buffer and absorption layers. The efficiency of the solar cell was improved to ɳ=4.29%, fill factor FF=52.92%, open circuit voltage Voc=0.74 V and short circuit current Jsc=10.8 mA/cm²

Molecular Characterization Of Wild Pleurotus Ostreatus (Mw457626) And Evaluation Of Β-Glucans Polysaccharide Activities, Ghazwan Q. Hasan Ph.D., Shimal Y. Abdulhadi Ph.D.

Karbala International Journal of Modern Science

Pleurotus ostreatus is a common cultivated edible mushroom worldwide. The fruiting bodies of P. ostreatus is a rich source of a β-glucans polysaccharide. The current study aimed to investigate the effectiveness of β-glucans as a natural polysaccharide produced by P. ostreatus as an antioxidant, antimicrobial, and anticancer. The molecular identification of P. ostreatus isolate was confirmed by Internal Transcribed Spacer (ITS) sequence. The sequence alignment and phylogenetic evolutionary relationship of studied ITS sequence were performed against some deposited sequences in GenBank. The analysis of high-performance liquid chromatography (HPLC) as well as the result of fourier transform infrared spectroscopy …

Cocoa: Context-Conditional Adaptation For Recognizing Unseen Classes In Unseen Domains, Puneet Mangla, Shivam Chandhok, Vineeth N. Balasubramanian, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i.e zero-shot domain generalization). For models to generalize to unseen classes in unseen domains, it is crucial to learn feature representation that preserves class-level (domain-invariant) as well as domain-specific information. Motivated from the success of generative zero-shot approaches, we propose a feature generative framework integrated with a COntext COnditional Adaptive (COCOA) Batch-Normalization layer to seamlessly integrate class-level semantic and domain-specific information. The generated visual features …

Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Joshua Larson, Clark N. Taylor, Thomas Wischgoll

Faculty Publications

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of …

Machine Learning To Predict Sports-Related Concussion Recovery Using Clinical Data, Yan Chu, Gregory Knell, Riley P. Brayton, Scott O. Burkhart, Xiaoqian Jiang, Shayan Shams

Faculty Research, Scholarly, and Creative Activity

Objectives
Sport-related concussions (SRCs) are a concern for high school athletes. Understanding factors contributing to SRC recovery time may improve clinical management. However, the complexity of the many clinical measures of concussion data precludes many traditional methods. This study aimed to answer the question, what is the utility of modeling clinical concussion data using machine-learning algorithms for predicting SRC recovery time and protracted recovery?
Methods
This was a retrospective case series of participants aged 8 to 18 years with a diagnosis of SRC. A 6-part measure was administered to assess pre-injury risk factors, initial injury severity, and post-concussion symptoms, including …

Learning Temporal Rules From Noisy Timeseries Data, Karan Samel, Zelin Zao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan A. Essa, Le Song

Machine Learning Faculty Publications

Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose higher level composite events. Examples of a composite event are a patient's medical symptom or a baseball player hitting a home run, caused distinct temporal orderings of patient vitals and player movements respectively. Such salient composite events are provided as labels in temporal datasets and most works optimize models to predict these composite event labels directly. We focus on uncovering the underlying atomic events and their relations that lead to the composite events within …

Differential Privacy In Privacy-Preserving Big Data And Learning: Challenge And Opportunity, Honglu Jiang, Yifeng Gao, S. M. Sarwar, Luis Garza Perez, Mahmudul Robin

Computer Science Faculty Publications

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer …

Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore

SDSU Data Science Symposium

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …

Editorial Board

Karbala International Journal of Modern Science

No abstract provided.