Reviewerly/exHarmony huggingface dataset:
Affine medical image registration with fusion feature mapping in local and global

Abstract Objective . Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships of the features due to inductive biases, such as weight sharing and locality. This is not conducive to affine registration tasks. Therefore, the evolution of real-time and high-accuracy affine medical image registration algorithms is necessary for registration applications. Approach . In this paper, we propose a deep learning-based coarse-to-fine global and local feature fusion architecture for fast affine registration, and we use an unsupervised approach for end-to-end training. We use multiscale convolutional kernels as our elemental convolutional blocks to enhance feature extraction. Then, to learn the long-range spatial relationships of the features, we propose a new affine registration framework with weighted global positional attention that fuses global feature mapping and local feature mapping. Moreover, the fusion regressor is designed to generate the affine parameters. Main results . The additive fusion method can be adaptive to global mapping and local mapping, which improves affine registration accuracy without the center of mass initialization. In addition, the max pooling layer and the multiscale convolutional kernel coding module increase the ability of the model in affine registration. Significance . We validate the effectiveness of our method on the OASIS dataset with 414 3D MRI brain maps. Comprehensive results demonstrate that our method achieves state-of-the-art affine registration accuracy and very efficient runtimes.
Board 109: BYOE: Laboratory Exercise using Augmented Reality and Virtual Reality for Environmental Engineering Curriculum

is a professor of environmental engineering in the Department of Civil, Environmental and Architectural Engineering (CEAE) at the
Expression of model ion channel currents generated using neuron in silico software in real time using dynamic clamp

NEURON is the leading in silico software for generating computational models of excitable cells (www.neuron.yale/edu/neuron/). We have previously demonstrated the combining of our real-time, dynamic clamp system with NEURON allowing for interfacing of living cells with NEURON models during electrophysiological recording. In the past, we have shown that real ion channel currents expressed in living cells can be input into NEURON models in real-time. We have now added the ability to input individual NEURON ion channel model currents into living cells. We performed electrophysiological recordings of hiPSC GABA neurons (hiPSC-GNs) (Fujifilm cellular dynamics) using the whole-cell ruptured patch clamp configuration. Insufficient background K+ current expression typically observed in hiPSC-GNs results in depolarized resting membrane potentials (RMP = −29.6 + 0.9 mV, n = 40) and the inability to record stable action potentials (APs). To restore the RMP to physiological values in hiPSC-GNs, we virtually expressed two K+ channel currents, Kv1 and Kv4, from a NEURON model of a Purkinje neuron (Akemann and Knopfel, 2006). Virtual expression of Kv1 or Kv4 hyperpolarized the RMP to −54.6 ± 0.8 (n = 4) and −60 ± 1.6 (n = 4), respectively and physiological AP morphology was restored (Kv1: amplitude = 80.2 ± 4.2 mV; AHP amplitude = 25.9 ± 2.8, APD90 = 2.3 ± 0.1, n = 4; Kv4: amplitude = 85.5 ± 9.1 mV; AHP amplitude = 15 ± 3.2, APD90 = 2.7 ± 0.1, n = 4). Given the vast NEURON model database (ModelDB, https://modeldb.science/), the ability to virtually express a wide variety of ion channel models in living cells can easily be accomplished with our dynamic clamp system allowing electrophysiologists to expand their research capabilities.
Movie Recommendation using Multiple Data Sources

&lt;p&gt; &lt;/p&gt; &lt;p&gt;Most of the recommender systems are built for the content or item providers. For example, Netflix recommends movies or TV shows, Amazon recommends books or other items being sold on Amazon, Facebook or Twitter recommends popular posts or tweets, Pinterest recommends related pins, YouTube recommends videos, etc. Most of these recommender systems are designed based on the usage data collected on their own websites.&lt;/p&gt; &lt;p&gt;However, sometimes it could be helpful if we could get information about recommended items from multiple data sources, providing multiple perspectives for users to make their decisions. In this research work, we study different approaches to integrate multiple data sources and the effect on the recommendation results when multiple data sources are used to recommend items. We propose a multiple data source-based movie recommender system that uses MovieLens rating data, YouTube movie trailer data, Netflix rating data, and tweets from Twitter. The user feedback data such as likes, dislikes, comments on movie trailers posted on YouTube can be helpful side information for movie recommender systems. In this research work, we study the effect of adding these side information to the movie rating data. Our proposed recommendation framework can integrate the trailer and rating data adopting various integration strategies: integrating all the trailer data as movie features, using sentiment scores derived from the trailer comments as a rating matrix to integrate with the movie rating matrix, and treating others as the movie features, or only integrating the sentiment score based rating matrix with the movie rating matrix. Our experiment shows that if we include the movie trailer data, recommendation accuracy is improved. We also find that the most accurate result is achieved if all the trailer feedback data is integrated as movie features. We use both Matrix Factorization (MF) and Deep Neural Network (DNN) Models to design our system. We find that the DNN model performs better than the MF model. Our results show that when we include multiple data sources to recommend items using the DNN model, the recommendation accuracy (F1 score) is increased by 41% on average comparing to the case when only one data source is used.&lt;/p&gt;
News Recommender System Considering Temporal Dynamics and Accuracy-Diversity Tradeoff

&lt;p&gt;News recommender systems aim to personalize users experience for online news readers and help them discover relevant and interesting news items from a broad and diverse search space. However, recommending news is a challenging task. There are hundreds of news articles published every day, many of which quickly become obsolete and irrelevant to the readers. Readers’ preferences (interests) also exhibit dynamic behavior and the relevance of readers’ preferences strongly depend on the context. Some of the readers’ preferences are long-term, reflecting the personality or behavior, whereas others are short-term, showing their current interests. External events, such as breaking news and trends, also influence readers’ interests. Although the high recommendation accuracy is appreciated, focusing on it too much sacrifices diversity and limits readers’ options. &lt;/p&gt; &lt;p&gt;The main contribution of this research is to design a recommender system to tackle the specific challenges of news recommendations. To address these challenges, we propose a recommendation strategy that seamlessly integrates readers’ long-term and short-term preferences when recommending news items. In addition to high accuracy, we focus on promoting reasonable diversity in news recommendations. To achieve a balanced objective (high accuracy and reasonable diversity), we formulate a combined optimization strategy that includes both of these aspects in the recommendation process. &lt;/p&gt; &lt;p&gt;More specifically, to achieve high accuracy, we propose novel latent factor models such as matrix factorization and a generalized linear model. In addition, we propose a regularized latent factor model to achieve reasonable diversity while maintaining high accuracy. Then, for the same purpose, we propose a deep learning-based framework composed of network components of news modelling and reader modelling. We use different regularization terms in the latent factor model to achieve accuracy-diversity balance, and the neural attention mechanism in deep neural networks for the same purpose. &lt;/p&gt; &lt;p&gt;We also introduce a new evaluation metric to measure the tradeoff performance with respect to accuracy and diversity. Experiments on real-world data have demonstrated the effectiveness of our proposed approach on quality factors such as accuracy, diversity as well as this tradeoff metric. Our model has achieved an improvement on this new tradeoff metric by 10-30% when compared to traditional and state-of-the-art recommendation algorithms.&lt;/p&gt;
Smart HomeKit-Enabled Peephole Camera

It can be difficult to satisfy the need for smart home convenience devices without invasive procedures that would go against living contracts in an apartment, dormitory, or any rented vacant living area. Creating a device that allows for convenience and security is imperative. The objective of this project is to develop and test a Smart Peephole Door Camera to identify who is at the door. A small, portable 3D printed enclosure that houses a Raspberry Pi, portable battery, and camera fit over a door's peephole on the inside of the door through a non-invasive installation process via 3M Command Strips. This allows the resident to conceal the camera from any outside visitors. The device connects to the user's home network via Wi-Fi to allow access to Apple's HomeKit API to enable remote viewing from a compatible iOS device. In addition, an open-source platform provided by Apple on GitHub with the addition of the HomeBridge lightweight NodeJS server allows seamless integration between the smart peephole camera and the user's iOS device. 3D CAD files created on SolidWorks and Shapr3D were printed and tested through multiple design iterations to ensure that the design of the enclosure and the mounting methods meet the fundamental constraints of this project.
Ensemble memory of a scene interacts with current perception regardless of attentional requirements.

How do we maintain a rich and stable perceptual experience across the entire visual scene, even when we are focusing on a subset of visual inputs? The current study explored this question by investigating whether the visual system processes summary statistics of multiple features regardless of task relevance, and how they interact with subsequent perception. To test the processing of multifeature summary statistics under different attentional requirements, we presented multiple Gabor patches with heterogeneous orientations/colors and asked participants to attend to a single feature dimension (Experiments 1 and 3) or a single item (Experiment 2) for the memory task. During the memory maintenance period (before memory response), we asked the participants to perform a discrimination task (Experiments 1 and 2) or a boundary localization task (Experiment 3) to test how the memory of the ensemble representation alters the subsequent perceptual experience. We found evidence for obligatory processing of scene summary statistics presented for the memory task, which interacted with the subsequent perceptual sensitivity. Specifically, not only summary statistics relevant but also those of task-irrelevant feature (Experiments 1 and 3) and outside the focus of attention (Experiment 2) were encoded and bidirectionally interacted with subsequent perception. These results suggest obligatory processing of summary statistics of a scene, which may allow rich and stable visual experience by integrating temporally adjacent visual inputs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Hemodialysis Wastewater Treatment via Electrocoagulation and Electro‐Oxidation: Modular Pilot‐Level Modeling and Simulation

Abstract Hemodialysis treatment in specialized clinics within the same hospital significantly impacts environmental water health due to contaminated wastewater. The issues observed included changes in electrical conductivity, the presence of dangerous bacterial loads, toxicity from heavy metals, total cyanide content, and helminth parasite eggs. The level of damage is dependent on the patient's health under treatment. This research will use a modular system that employs electrocoagulation and electro‐oxidation processes at the laboratory and pilot levels to treat hemodialysis wastewater using synthetically prepared and real samples extracted from local clinics. The results showed that these hybrid systems improved various physicochemical parameters. Specifically, decreases in electrical conductivity of 49 %, total suspended solids of 27–100 %, chemical oxygen demand of 49 %, biochemical oxygen demand of 49 %, and cation and anion loading were observed at 96–100 % and pH 8.13 UpH in accordance with the established standards. With these results and the experimental conditions used, the proposed treatment system was modeled using the GPS‐X program, and it was concluded that the modular system used and the electrocoagulation/electro‐oxidation/activated carbon configuration is suitable for treating wastewater from hemodialysis and that scaling up this process to facilities that have dialysate machines more advanced than those considered in this work is possible.
Bulletin WKU

Distance education is an urgent problem in the world field of education and science. The abolition of the correspondence form in our country has created a problem for citizens who, while working or due to health, family, social status, set themselves the goal of improving the level of professional education. In the course of the research, the directions of flexible transformation of Kazakhstani and foreign best practices are considered, the purpose of which is to solve the problem of distance learning in the higher education system through the introduction of part-time study. The authors study the experience of distance learning in the USA and European countries and present it with quantitative and qualitative data. The research uses methods of analysis of foreign experience historical, chronological, social, pedagogical scientific works, academic reports. Pedagogical conditions aimed at introducing the best of foreign experience in part-time study into the Kazakh higher education have been formulated. The article clearly presents trends in the transformation of foreign digital education experience into the higher education system in Kazakhstan. An exploration focused on the development of the part-time teaching methodology is still ongoing and offers a rational methodology in the future.
Built Custom Alexa Skills using AWS

Abstract:The creation of unique Alexa skills using the Amazon Web Services (AWS) infrastructure is the focus of this project. Alexa skills are voice- activated features that improve Alexa, the virtual assistant from Amazon, in terms of functionality. The project's main goal is to demonstrate the flexibility and adaptability of AWS in voice- activated interactions by developing custom skills that are suited to certain use cases or applications. During the development process, the bespoke Alexa skills are hosted and executed using AWS Lambda, an event-driven computing service. With AWS Lambda, code can be seamlessly integrated without requiring server management, guaranteeing a scalable and effective solution. The stability and dependability of the custom Alexa skills are further enhanced by the extensive suite of tools and services offered by AWS, including the Alexa Skills Kit (ASK), DynamoDB for data storage, and IAM (Identity and Access Management) for security. The complexities of voice-activated technologies, AWS infrastructure, and Alexa skill development are investigated through this research. With the help of the Alexa ecosystem and customized skills, companies, developers, and enthusiasts can use AWS's power to create unique and interesting voice experiences. The project's main ideas are summarized in this abstract, which highlights the use of AWS technology to develop customized and inventive Alexa skills. Keywords:Custom Alexa skills, AWS, Voice- activated technology, Alexa Skills Kit, AWS Lambda, DynamoDB, IAM.
A novel demonstration of preparation in pop-out search.

There is an ongoing debate among visual attention researchers about whether top-down processes contribute to pop-out search. In the present study, we describe a new method to orthogonally manipulate top-down preparation and feature priming in a pop-out search task. On each trial, participants viewed a single-item (randomly blue or orange) followed by a pop-out search display (randomly blue target with orange distractors, or vice versa). Preparation was induced by instructing participants to respond to the single-item if it was a particular colour and to ignore it otherwise-but to respond to the odd-coloured target in all following pop-out search displays. This method allowed us to examine whether top-down preparation for the single-item influenced subsequent pop-out search. Our results revealed a large effect of preparation for the single-item on subsequent search response times. We discuss this result in relation to the interplay between top-down control and selection history effects in pop-out visual search. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Optical bistability and flip-flop function in feedback Fano laser

Optical bistability has the potential to emulate the capabilities of electrical flip-flops, offering plenty of applications in optical signal processing. Conventional optical bistable devices operate by altering the susceptibility of a nonlinear medium. This method, however, often results in drawbacks such as large device size, high energy consumption, or long switching times. This work proposes an optical bistable device incorporating strong optical feedback into a Fano laser. This leads to multiple stable states and introduces a region of bistability between the inherent Fano mode and a feedback-induced Fabry-Perot mode. Unlike conventional bistable devices, the Fano system exploits strong field localization in a nanocavity to control the properties of one of the laser mirrors. This configuration means that switching states can be achieved by modulating the mirror's loss rather than changing the susceptibility of the active medium. Importantly, modulation can be implemented locally on a nanocavity, bypassing the need to adjust the entire laser system. This leads to fast flip-flop actions with low energy consumption. The feedback Fano laser can be embodied in a compact microscopic structure, thus providing a promising approach towards integrated all-optical computation and on-chip signal processing.
Sentiment Analysis

Abstract Project Members • Ravin Jajistar • Sagar Sathynarayanan • Michael Shiferaw Sentiment Analysis: JSS Soultions LLC Sentiment Analysis, is a computer program that assesses data from user experience, and categorizes them as positive, neutral, and negative in order to improve a service or product. In this project, individuals will develop a program that will grab tweets from twitter feeds, and run these tweets against a dictionary and categorize the tweet as positive, negative, and neutral. The program will then generate a report of the feedback, and if properly analyzed and assessed correctly, companies will be able to improve on their services, or products. Part of the project is establishing a small consultant business, mainly a LLC under the name JSS Solutions LLC and then going out and signing clients in order for us to help provide to improve their service or product. The product will be first tested among the Morgan state community on the safety around campus, hence contributing towards the community.
Heuristic Planning for Continuous Systems in Hybrid Temporal Situation Calculus

&lt;p&gt;Given a description of domain and its dynamics, temporal numeric planning attempts to find a sequence of actions that satisfies a given set of constraints for a dynamical system. Current planners operate on grounded transition systems and discretized representations of the domain which lead to poor scalability. Furthermore, given the problem’s difficulty, most modern planners restrict their capabilities to a subset of hybrid domains, e.g. support for only polynomial evolution of numeric state variables and linear action conditions. To address these concerns, we present a lifted planner, NEAT (Non-linEAr Temporal) Planner, that utilizes a logical description of the domain described in Hybrid Temporal Situation Calculus. Furthermore, we develop AMPLEX, an interface to AMPL and several non-linear programming solvers, which allows us handle several non-linear functions. We also present a novel non-linear programming based heuristic to improve scalability. Lastly, we perform a detailed comparison between current state-of-the-art solvers and our planner.&lt;/p&gt;
Intelligent deep learning-based disease monitoring system in 5G network using multi-disease big data

Recently, real-world disease monitoring techniques designed based on wearable medical equipment efficiently minimize the mortality rate. Initially, the data are manually collected from the patients to predict five diseases using 5 G frameworks. Then, the collected data are pre-processed to obtain high-quality data using the techniques like contrast enhancement, median filtering, fill empty space, remove repeated value and stemming. The pre-processed data are taken for extracting the features using a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the deep features. The parameters like hidden neuron count and epoch are tuned by the proposed Modified Predator Presence Probability-based Squirrel Search-Glowworm Swarm Optimization (MPPP-SSGSO) algorithm to enhance the variance. Then, the extracted features acquired using the 1D-CNN are given to the ensemble boosting-based models for predicting the score, which is combined by comprising approaches like Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost) and Category Boosting (CatBoost). Further, the predicted scores obtained from such models are concatenated and passed to the Ensemble Boosting Scores-based Fuzzy Classifier (EBS-FC) for classifying the five different diseases. Here, the membership function of the fuzzy is optimized by the same developed MPPP-SSGSO algorithm for enhancing accuracy. Experiments are conducted, and validation is performed, which showcased that the recommended framework achieved a better outcome rate than the conventional techniques. Finally, the suggested strategy outperforms the current state-of-the-art methods with an accuracy rate of 91.34%.