Reviewerly/exHarmony huggingface dataset:
A Qualitative Insight into User Experiences of an Intelligent Tutoring System to Learn Sketching Skills.

Authors:
raw_author_name: Kerrie Douglas
raw_affiliation_string: Cornell University
raw_author_name: Tracy Hammond
raw_affiliation_string: Texas A&M University

Abstract: Abstract Sketching is a valuable skill for many engineering students to support the development of various auxiliary skills such as refined spatial visualization, problem-solving, idea generation, and communication. As the students engage in the engineering design process, sketching skills, along with the auxiliary skills, become valuable tools in future courses and continually in their careers. Explicitly teaching students to sketch is challenging given the instructor-to-student ratio. Hence, intelligent tutoring systems (ITS) are highly beneficial to students in this context to develop these skills actively rather than expect students to develop these skills independently through the needs of other courses. An ITS was introduced through this study to teach sketching skills to students in mechanical engineering courses. The basics of Two-point perspective sketching were the focus of the instruction material facilitated by this ITS. The tutoring platform provides individualized automatic feedback to students immediately after they complete a sketch to inform them of their performance and ultimately to enhance their sketching skill development. This study aims to understand the experiences of graduate and undergraduate mechanical engineering students from three institutions learning sketching through the ITS environment. Our study is guided by the following research questions: 1. What was the engineering student's experience in learning to sketch in an intelligent tutoring platform? 2. What are the strengths, weaknesses, and suggestions for improving the intelligent tutoring system? 3. What are the impacts of the intelligent tutoring System on the sketching self-efficacy of engineering students? In this study, researchers collected data through surveys and semi-structured interviews. The participants were students enrolled in undergraduate and graduate Mechanical Engineering courses at three different institutions where they learned and practiced sketching using the ITS. This study helps us to understand the strengths and weaknesses of the ITS, along with suggestions for how to improve the software from an engineering student perspective. The user experiences of mechanical engineering students were valuable to understand if and how students are finding this particular ITS helpful in their academic lives. The results of the study will be useful to researchers and the engineering education community working to develop educational software tools, to better understand student expectations, and educators interested in identifying a way to incorporate sketching.
Movie Recommendation using Multiple Data Sources

Authors:
raw_author_name: Debashish Roy

Abstract: &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

Authors:
raw_author_name: Shaina Raza

Abstract: &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

Authors:
raw_author_name: Saurav Basnet
raw_affiliation_string: Program of Electrical and Computer Engineering Wentworth Institute of Technology Boston, MA, 02115, USA

Abstract: 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.

Authors:
raw_author_name: Sang Chul Chong
raw_affiliation_string: Graduate Program in Cognitive Science, Yonsei University.

Abstract: 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).
Built Custom Alexa Skills using AWS

Authors:
raw_author_name: Preksha Pratap

Abstract: 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.

Authors:
raw_author_name: Hongjin Sun
raw_affiliation_string: Department of Psychology, Neuroscience and Behaviour, McMaster University.

Abstract: 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

Authors:
raw_author_name: Jesper Mørk
raw_author_name: Yi Yu

Abstract: 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.
Movie Recommendation using Multiple Data Sources

Authors:
raw_author_name: Debashish Roy
raw_affiliation_string: B.Sc. in Computer Science and Engineering, Shah Jalal University of Science and Technology, Bangladesh, 2005; Computer Science Toronto, Ontario, Canada, 2021; Computer Science, Ryerson University, Canada, 2015 M.Sc. in Computer Science,; Uppsala University, Sweden, 2007

Abstract: &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;
Intelligent deep learning-based disease monitoring system in 5G network using multi-disease big data

Authors:
raw_author_name: Anupam Das
raw_affiliation_string: Department of CSE, The Assam Royal Global University, Betkuchi, Guwahati, Assam, India

Abstract: 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%.
Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Authors:
raw_author_name: Dilber Uzun Ozsahin
raw_affiliation_string: Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates; Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
raw_author_name: Ilker Ozsahin
raw_affiliation_string: Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
raw_author_name: Berna Uzun
raw_affiliation_string: Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey

Abstract: Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.