Journal of
Systemics, Cybernetics and Informatics

 ISSN: 1690-4524 (Online)    DOI: 10.54808/JSCI


A Sign Language Learning Application for Children with Hearing Difficulties
Kuniomi Shibata, Akira Hattori, Sayaka Matsumoto
Pages: 1-6
The purpose of this paper is to develop a mobile application to support both sign language and literacy skills among children with hearing difficulties as part of rehabilitation engineering and to implement its basic functionality. This study will make it possible to support children with hearing difficulties in learning written language in combination with sign language, thereby emphasizing the importance of the latter, and in learning and communicating with their parents who use spoken language. This application has the following functions: (1) to register sign language clips acquired from a mobile device’s camera by attaching tags to them, (2) to save multiple sign language clips using sets and tags, and (3) to reproduce and play back sequences of the saved sign language clips. Because of a preliminary evaluation experiment, the application was highly evaluated by the collaborators, and most of the negative comments were attributed to the small amount of registered data and its inability to support use in complex situations. Based on this, we consider that the basic concept of this application has been successfully realized.

An Experience Mapping Method for Delayed Understanding in STEM Education
Masaaki Kunigami, Takamasa Kikuchi, Takao Terano
Pages: 7-16
This study introduces a novel experience-mapping methodology designed to alleviate the challenge of delayed comprehension in education. Education often entails a delayed understanding of its content and value. This comprehension lag often results in discrepancies between learners and educational content, potentially leading to setbacks in the learning process. In response, we present a mapping model that delineates the essential structure of educational content and positioning between the learner and the content. This model serves as a guiding roadmap, enabling learners to navigate the complexities of educational content through a pair of constructed semantic networks. These networks reflect insights from recent brain science and educational experience studies. This study delves into the application of the STEM (Science, Technology, Engineering, Mathematics) education. Furthermore, we discuss the potential of experience mapping within the spheres of curriculum design and faculty development. Through these applications, this research contributes to the development of educational.

Refining the Art of Judgment Education: Evaluation of an Educational Case Study on Making Judgments About the Pros and Cons of COVID-19 Vaccination During the Pandemic
Ariyoshi Kusumi, Yasukazu Hama
Pages: 17-22
We discussed the form of education that fosters rational judgment based on the selection and prioritization of a large amount of information. Specifically, we developed a lesson plan for fostering judgment skills focused on the theme of the pros and cons of COVID-19 vaccination for prevention. We sorted out the twelve requirements for classes from three perspectives: education for fostering judgment, risk education, and critical thinking education. Based on the extent to which the twelve requirements were reflected in the course design, the course was evaluated on two aspects: "A: education that promotes subjective judgment without scientific or logical errors" and "B: education to achieve desirable judgment through communication.” As a result, it was evident from the questionnaire survey evaluation that B was sufficiently achieved. On the other hand, the effectiveness of A resulted in different outcomes between student questionnaire survey evaluations and instructor assessments of the reports. In other words, while the student survey indicated sufficient achievement, the instructor evaluation indicated that it was not sufficient. From this, it is inferred that some simplification is important at least as an educational practice in this university.

A New Digital Culture in Architecture and Engineering Design Classes with Technological Advances
Mozart Joaquim Magalhães Vidigal, Renata Maria Abrantes Baracho, Marcelo Franco Porto
Pages: 23-28
This work deals with the approach of information and communication technologies in the classroom activities of the disciplines of architectural projects to propose improvements in the formation of architects and in the teaching models practiced in schools of Architecture and Engineering of Belo Horizonte, Minas Gerais, Brazil. The objective of this work is to discuss the use of technology through the design course. The position of Professors in the adoption of these technologies was analyzed. The work presents an analysis of pedagogical practices to reflect on possible advances, disharmonies and conflicts with the use of technologies in design development. This discussion can contribute to stimulating the qualification of Professors and future professionals. The findings indicate that new pedagogical practices with the use of technology can be gradually adopted. So Professors and students can better interact and develop the teaching/learning relationship with the updated models of teaching architectural projects.

Using Federated Learning for Collaborative Intrusion Detection Systems
Matteo Rizzato, Youssef Laarouchi, Christophe Geissler
Pages: 29-36
Neural networks have become cutting edge machine learning models for detecting network attacks. Traditional implementations provide fast and accurate predictions, but require centralised storage of labelled historical data for training. This solution is not always suitable for real-world applications, where regulatory constraints and privacy concerns hamper the collection of sensitive data into a single server. Federated Learning has recently been proposed as a framework for training a centralised model without the need to share data between different providers. We use the CICIDS2017 dataset provided by the Canadian Institute of Cybersecurity to demonstrate the benefits of Neural Networks-based Federated Learning for the detection of the most relevant types of network attacks. We conclude that a federated-trained neural network outperforms locally-trained models (at isoarchitecture) in terms of F1-score and False Negative detection ratio. Further, such model has a minor loss of performance and convergence rapidity compared to a model trained over a hypothetical centralised dataset.

Design and Development of an Application for the Generation of Garment Patterns Based on Body Measurements Using CNN
Geraldine Curipaco, Jeiel Tarazona, Daniel Subauste
Pages: 37-46
In recent years, the growing consumption of products and services over the Internet has led companies to take their business models to virtual platforms, so retail fashion companies, such as ateliers, need to adapt to new market trends. Ateliers specialize in making garments to the customer's measurements, so this process requires a high level of time, cost and personnel specialized in taking body measurements and pattern making. There are several technological solutions to obtain body measurements and other solutions to obtain garment patterns, however there are no solutions that integrate both processes. Therefore, we propose an application to produce dress garment patterns tailored to a person from photos, using image processing and convolutional neural networks (CNN). Our proposal starts by obtaining a frontal and a lateral photo of the person, as well as her height, and then processing the images to obtain the body measurements by means of a CNN. Finally, we proceed to adjust the patterns of the garment required with the measurements obtained in order to give as a final result the dimensions of the dress's garment patterns.

Data-Driven Security Measurements to Improve Safety in NYC and NJ Mass Transit
Nithya Nalluri, Michael Bsales, Christie Nelson
Pages: 47-55
Public transit in America in recent years is potentially vulnerable to terrorist or mass casualty attacks. These vulnerabilities are in part due to the lack of strict screening and content policing, unlike security at airports, but also their attractiveness as a potentially high-value target. Although current public transit systems are designed to efficiently allow passengers to quickly travel, screening of individual riders for weapons remains limited due to current technology limitations and high peak throughput requirements. This paper aims to develop an understanding of the current state of security check systems as applicable to high-traffic subway stations. We also worked towards creating a proof-of-concept risk analysis model using crime and other types of publicly available data for the New York City and New Jersey transit regions.

A Review on Security and Privacy of Smart Cities
Abdulhakim Alsaiari, Mohammad Ilyas
Pages: 56-62
Smart cities are expected to provide better services to citizens and urban environments, and to enhance the quality of their daily life. By utilizing smart technologies, Smart cities can deal with the emerging urbanization issues and promote sustainable development. Despite this, security and privacy issues are considered an obstacle which can impact the success of such emerging technologies and smart systems in smart cities. However, to fully leverage the benefits of smart systems and promote their further development, it’s imperative to understand the security and privacy threats that weaken systems and make them vulnerable to be attacked. Motivated by these factors, this literature review provides a useful comprehensive combination of related literatures on smart cities challenges by technically analyzing various results. This review also discusses several components of smart cities such as transportation, governance, people, living, economy, innovative architecture, and associated knowledge and ideas. This paper also aims to critically examine various existing and deploying security and privacy protection methods for smart cities. Finally, we highlight several unresolved challenges and suggest future research possibilities and current security requirements, which may help building secured, privacy-protected, and stable smart cities.

Use of Audience Response Systems to Enhance Student Engagement in Online Synchronous Environments: An Exploratory Study
Trevor Nesbit, Angela Martin
Pages: 63-68
Many higher education institutions adapted to the Covid-19 pandemic by switching their teaching into online mode making use of online synchronous sessions using technologies such as Zoom. It was common for lecturers to find it disconcerting that many students were not turning on their cameras and microphones and how this made it difficult to ascertain whether their students were engaged in the sessions at all.

This paper examines the experiences of teaching staff in eight courses relating to the use of audience response systems (ARS) to improve the experiences of students and teaching staff when conducting synchronous online teaching sessions or hybrid sessions when they had some face-face students and some online students joining sessions synchronously.

Literature is examined that shows the benefits relating to the use of ARS in synchronous online teaching sessions to include anonymity of student responses; enhancement of feedback between teaching staff and students; and teaching staff getting a better sense of student engagement during a session.

An analysis of the eight cases presented confirms these benefits in the literature from the perspective of the teaching staff. The findings apply irrespective of the ARS being used and will be of relevance and interest to any teaching staff seeking to improve the experiences of students and teaching staff involved in synchronous online teaching sessions.