To develop quality crowdsourcing systems, aggregating responses from workers is a critical issue. However, it has been difficult to construct an automatic mechanism that flexibly aggregates worker responses in natural language. Accordingly, responses need to be collected in a standardized format, such as binary-choice or multiple categorizations, to avoid large aggregation costs. Recently, with the advent of large language models (LLMs), natural language responses can be automatically and flexibly aggregated. We propose a framework that uses LLMs to flexibly aggregate natural language responses from workers and, as a promising example, consider this framework for crime detection from surveillance cameras using crowdsourced cognitive abilities. In an experiment using subjective evaluation, our proposed framework is shown to be effective for automatically aggregating natural language responses from crowd workers.

The world has entered a new normal in response to the crisis caused by the COVID-19 pandemic. This new normal has unique challenges and opportunities for the faculty, as physical campuses have gradually re-opened for teaching and learning. Although a growing amount of research has shown a relationship between the extent of basic psychological needs satisfaction and the state of well-being in diverse populations, studies focused on faculty in the new normal remain limited. An online survey of 100 faculty members from an institution of Catholic higher education in the Philippines was conducted in the latter half of 2022 to examine this relationship. The results of this study showed that satisfying the basic psychological needs of faculty during the new normal can contribute significantly to their well-being. These results can inform higher education institutions about how they can best support their faculty in the new normal and promote student learning.

Health informatics as a topic is rarely introduced to middle school students due to their age and insufficient background knowledge in computing and healthcare. At the same time, it has been observed that many students have lost interest in science and technology when they reach high school. Funded by the NIH AIM-AHEAD initiative, we embarked on a project to create a health informatics after-school initiative focused on AI. We recognize that students who identify as racial or ethnic minorities are less likely to be introduced to and less prepared for a range of STEM-H careers. Limited diversity in the life sciences and health professions has significant consequences for access to healthcare services. Preparing diverse students for the future "digitally proficient" healthcare workforce is fundamental to addressing health disparities, increasing cross-cultural communication, and positively impacting health equity. We acknowledge that students are more likely to thrive academically in areas of STEM- H when they have access to instructors from diverse races, ethnicities, and backgrounds who understand their experiences and perspectives.

The aim of this study is to explore the potential for creating a unified digital information space using a modern ERP system to manage all processes and resources of territorial communities, which are categorized as non-industrial enterprises. This research is conducted in the context of building a modern landscape of Industry 4.0 technologies, which are considered to be the future of industrialization. The practical case of Ukraine is used to illustrate the typical problems associated with the uncoordinated use of different types of software in the management of enterprises and organizations operating in territorial communities. Furthermore, the advantages of switching to a new ERP platform are discussed. The benefits of deploying the system's multi-tier architecture in the cloud and implementing a corporate model for parallel management of individual divisions and organizations are also highlighted.

Since global crises humanity faces are becoming a regular phenomenon, the need to research the mechanisms for reaching business resilience has become crucial. Therefore, this paper explores the factors that influence one component of business resilience: companies’ ability to adapt to changing circumstances with minimal destructive effect during crises. The literature review revealed three domains of company adaptability factors - individual, organizational, and external. The qualitative content analysis of responses from 218 heads and owners of companies from Europe, Asia, Africa, and America enabled us to determine factors that have the most significant weight in business adaptability The three tables that systemize 49 categories provide insights into the factors that impact business adaptability, based on the experiences of business practitioners during the Global Financial Crisis of 2007-2008, the COVID-19 pandemic, and Russia's war in Ukraine.

Throughout the years, the voting process has under-gone a digital transformation, aiming to achieve greater control and optimize time while ensuring the integrity of each vote. While previous solutions have introduced changes in architecture and security processes, there hasn’t been a defined secure model for voting. The applications developed in this study employed biometrics and a fingerprint reader for authentication security, along with cryptography algorithms to safeguard the flow of voting data. Experts and individuals involved in the Peruvian electoral process evaluated the web and mobile applications to determine their viability in a real-world context and their potential to enhance the electoral process in Peru. This evaluation was conducted through a case study involving 50 participants and satisfaction surveys, which qualitatively assessed the usability and effectiveness of the applications. The results indicated that the developed applications were well-received, perceived as intuitive, and provided an interactive experience.

The purpose of the research is to explore Gennerations Z's and millennials' perceptions of masculinity in advertising. This can help advertisers to understand what type of masculine character to focus on and whether advertisers' offered version of masculinity is in alignment with consumer preferences. Research methods consisted of an extensive literature review process and quantitative research methods such as survey research of younger consumer segments such as Generation Z and millennials. The empirical results were analyzed using the SPSS 23 statistical software program. The research found that consumers tend to approve of modern masculinity in advertising more than traditional ones, with women approving of modern in slightly more convincing numbers than men. The research also found that consumers give preference to the display of affection and love and depicting masculinity less stereotypically in modern masculinity advertisements.

A design policy was established for a specific data flow and learning method for the automatic heart-sound diagnosis system under development. The production of each part becomes possible, and auscultation and learning begin. It can be used over clothes as long as it is applied well to the skin surface of the chest. It would be nice to be able to set multiple auscultation positions, but there is a limit to what ordinary people can be asked to do, so this should be considered while having AI learn.

We analyzed normal heart sounds to explore learning strategies. Sounds I and II are considered to be important anchor information sources for identifying other heart sounds. Abnormal heart sounds may not be heard at every beat and the rhythm may be abnormal. AI refers to the multiple beats of heart sounds during auscultation. Heartbeat analysis is a multidimensional information analysis related to time and space, and heart sounds are factored if normal and abnormal heart sounds can be organized based on the score. For pitch that tends to depend on individuals and devices, a relative discussion would be more appropriate.

This paper presents an evaluation of user satisfaction with the MOODLE platform using the Genetic Algorithm Multicriteria Satisfaction Analysis (GA-MUSA) method, and compares the results to the conventional MUSA method. A questionnaire was developed and administered to 100 participants (students and professors), and the data was analyzed using both methods. The results showed that the GA-MUSA method produced a higher overall satisfaction level compared to the Conventional MUSA method. The study also conducted a correlation analysis to determine the relationship between demographic variables and satisfaction levels. The findings suggest that MOODLE experience is the most important demographic variable related to satisfaction levels. The present study contributes to the existing literature by providing valuable insights into the use of GA-MUSA method to evaluate user satisfaction with educative software.

The Internet of Things (IoT) is considered one of the world’s fastest-growing technologies, and it has a tremendous impact on people’s lives in many different ways. With notable improvements in the evolution and expansion of technologies, the IoT faces numerous security threats and challenges. IoT technology uses various devices and protocols, making it difficult to apply adequate security control across the whole system and vulnerable to multiple attacks. Using essential technologies such as ML helps in addressing the recent security challenges and attacks on the IoT ecosystem. This paper presents an overview of IoT security. Also, it highlights types of IoT architecture security and explores the various kinds of attacks under each IoT architectural layer. Moreover, this paper discusses the uses of Machine Learning (ML) as a solution in IoT systems.

Tandem mass spectrometry (MS/MS) is a widely used technology for identifying metabolites. De novo metabolite identification is an identification strategy that does not refer to any spectral or metabolite database. However, this strategy is time-consuming and cannot meet the need for high-throughput metabolite identification. Böcker et al. converted the de novo identification problem into the maximum colorful subtree (MCS) problem. Unfortunately, the MCS problem is NPhard, which indicates there are no existing efficient exact algorithms. To address this issue, we propose to apply quantum computing to accelerate metabolite identification. Quantum computing performs computations on quantum computers. The recent progress in this area has brought the hope of making some computationally intractable areas trackable, although there are still no general approaches to converting regular computer algorithms into quantum algorithms. Specifically, there is no efficient quantum algorithm for the MCS problem. The MCS problem can be considered as the combination of many maximum spanning tree problems that can be converted into minimum spanning tree problems. This work applies a quantum algorithm designed for the minimum spanning problem to speed up de novo metabolite identification. The possible strategy for further improving the performance is also briefly discussed.