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    A Mobile Application for Taking Notes Based on Cornell Technique
    (Springer Science and Business Media Deutschland GmbH, 2024) Demirelli, Hasan; Isler, Yalcin Yalcin; Yuce, Yilmaz
    Notetaking is considered, by many educators, as one of the critical actions of learning. There are several note-taking methods and approaches. Based on these methods and approaches, various applications, whether mobile, desktop or -Web-based, were developed. In this paper, a novel note-taking application based on a technique, known as Cornell Technique, is presented. For the software development process, Incremental Model was adopted. Requirement Analysis included, aside from examining principles and related note-taking structure of Cornell Technique, investigating (i) how to perform notetaking as an activity of learning, (ii) its product and (iii) relationship of notes for the purpose of storage. Models containing sub-activities, such as reviewing note have been identified and some were selectively adopted and related functions such as review alert (tickler) and collaboration on notetaking have been implemented. To the purpose of storage, a tree-based scheme called collection was modelled. User interfaces were first designed as mockups and click-through prototype using Adobe XD. The mobile application was implemented in Dart programming language. Google’s Flutter Framework was adopted to have flexibility in UI development. The application has been published in Google Play Store for users to install for free. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
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    Sub Data Path Filtering Protocol for Subscription of Event Parts and Event Regeneration in Pub/Sub Pattern
    (Springer Science and Business Media Deutschland GmbH, 2024) Inanir, Serif; Yuce, Yilmaz
    Pub/Sub is a common pattern allowing a producer to publish events to consumers. In types of Pub/Sub, structure of an event is either identified by publishers based-on static rules or by consumers based-on filtering approaches. In both scenarios, actors’ total performance might get degraded due to required operations (e.g., filtering) impacting throughput. This study focuses on designing a filtering approach for both actors of Pub/Sub by reducing data size to be transmitted by producers and received and processed by consumers by creating a loosely coupled context, in which horizontal alterations to structure of any event can occur. Sub Data Path (SDP) approach presents a matching tree to separate an event with scope like JSON data, and each key in the relevant event act like a topic without being defined as a topic. Thereby, producers only must transmit part of a message through a path on the event structure to be located into a former event to create new event; consumers can subscribe to any subtopic (key for JSON format) to be able to receive data in terms of its own mechanism, not a producer’s design. Therefore, creating an event can be completed with different producers which contribute a piece of the whole event; Bounded Context structure belongs to microservice architecture as a decomposition strategy can be handled by consumers in relation to their own business logic. To measure the proposed method, an experiment with gaze points collected by an eye tracker has been designed. By performing the filtering method for one, two and maximum SDP keys, filtering duration, event size reduction percent and transmission duration were revealed. The experimental results imply that the proposed method can send 7.5 events in average, instead of sending just one in the same period. Also, since worst case of the proposed method based-on events in the context can be calculated, an architecture can be prevented from bottlenecks. These benefits makes SDP advantageous over similar methods in terms of being both a fast and scalable alternative. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Subjective Cognitive Load Evaluation of a Mobile Personal Health Record Application
    (2023) Zayim, Neşe; Yildiz, Hasibe; Yuce, Yilmaz
    Mobile Personal Health Records (mPHRs), which make it possible to track and manage users' health information, can be an important aid in improving people's health. Despite its potential benefits, poor usability of systems can hinder the adoption and use of mPHRs. This study aims to evaluate the usability of a mobile health application in terms of perceived cognitive workload and performance. The cognitive workload experienced by 30 volunteers (15 experienced and 15 inexperienced), was measured while performing the given tasks with the NASA-Task Load Index (NASA-RTLX) scale, and the duration of the fulfillment of the tasks by eye tracking device. While there was no significant difference between the two user groups in the completion time of the tasks, a significant difference was found in the perceived cognitive load. "Making an appointment", which could take much longer to complete than other tasks, resulted in the highest cognitive load for all users. Further usability research using think-aloud protocols and user interviews could provide insights into design improvements for reducing cognitive load and enhancing performance.
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    SynapSign: An Advanced Machine Learning Framework for American Sign Language Recognition Utilizing a Novel Landmark-Based Dataset
    (Institute of Electrical and Electronics Engineers Inc., 2024) Uysal, Erdoğan; Balikci, İrem; Öztimur Karada?, Özge; Yuce, Yilmaz
    Hearing loss is a common condition affecting a significant proportion of the world's population, creating barriers to effective communication. Sign language, particularly American Sign Language (ASL), is an important tool for the social integration and personal growth of people with hearing loss. The need for effective tools to facilitate the learning and practice of ASL is increasingly recognized. Although there are many studies and proposed softwares that execute based on certain approaches such as recognition of signs by machine learning techniques with relatively high accuracy, it is apparent that there exists a need for higher performance. This paper presents SynapSign, a desktop application designed to enhance ASL learning using machine learning algorithms. To develop the application using the machine learning model having the highest accuracy, the performances of Random Forest, XGBoost and Deep Neural Network (DNN) classifiers were investigated. To this purpose, an image dataset consisting of 2600 images was prepared. For each letter of 26 letters for ASL, 100 images of hands showing 21 hand landmarks was built for accurate hand gesture recognition using Google's MediaPipe technology. Thereafter, the three classifiers were trained on this extensive dataset of ASL hand images. Acquired models were tested and their performances were compared based on accuracy, precision and recall metrics. The results reveal that the model of Random Forest classifier performs slightly higher for all three metrics with 99.6%, 99.3% and %99.7, respectively, than other models. Therefore, SynapSign was developed using this model with a user interface that allows user to input sign image from a video stream via a camera and labelled with 21 hand landmarks by MediaPipe Framework's default hand detection model. Compared to traditional methods, the application provides a more interactive and engaging learning experience, allowing users to practice and improve their ASL skills with real-time feedback. Our findings suggest that SynapSign could serve as a valuable tool for both educational and accessibility purposes, addressing the gap in resources available to ASL learners. © 2024 IEEE.