Computer Science Knowledge Research Articles

A smarter perspective: Learning with and from AI-cases

Artificial intelligence (AI) has only partially (or not at all) been integrated into medical education, leading to growing concerns regarding how to train healthcare practitioners to handle the changes brought about by the introduction of AI. Programming lessons and other technical information into healthcare curricula has been proposed as a solution to support healthcare personnel in using AI or other future technology. However, integrating these core elements of computer science knowledge might not meet the observed need that students will benefit from gaining practical experience with AI in the direct application area. Therefore, this paper proposes a dynamic approach to case-based learning that utilizes the scenarios where AI is currently used in clinical practice as examples. This approach will support students' understanding of technical aspects. Case-based learning with AI as an example provides additional benefits: (1) it allows doctors to compare their thought processes to the AI suggestions and critically reflect on the assumptions and biases of AI and clinical practice; (2) it incentivizes doctors to discuss and address ethical issues inherent to technology and those already existing in current clinical practice; (3) it serves as a foundation for fostering interdisciplinary collaboration via discussion of different views between technologists, multidisciplinary experts, and healthcare professionals. The proposed knowledge shift from AI as a technical focus to AI as an example for case-based learning aims to encourage a different perspective on educational needs. Technical education does not need to compete with other essential clinical skills as it could serve as a basis for supporting them, which leads to better medical education and practice, ultimately benefiting patients.

Development and Implementation of the Data Science Learning Platform for Research Physician.

Data analysis and their application are the unavoidable factors in the activities analyses in health care. Unfortunately, the acquisition of data from large available medical databases is a complex process and requires deep knowledge of computer science and especially knowledge of tools for data management. According to the European General Data Protection Regulation, the problem becomes much more complex. Recognizing these problems and difficulties, we have developed a Data Science Learning Platform (DSLP) that primarily targets practitioners and researchers but also the computer science students. Using our proposed tool chain together with the developed graphical user interface, data scientists and research physicians will be able to use available medical databases, apply and analyze different anonymization methods, analyze data according to the patient's risk and quickly formulate new studies to target a disease in a complex data model. This article presents a clinical research discovery toolbox that implements and demonstrates tools for data anonymization, patient data visualization, NLP-tools for guideline search and data science learning tools.

Design and implementation of the interdisciplinary curriculum for intelligent chemical engineering program at Taiyuan University of Technology

High quality personnel (HQP) with interdisciplinary knowledge and skills of chemical engineering, computer science, and automation are urgently needed for intelligent chemical engineering. This study explores how to make chemical engineering students proficient in applying automation and computer knowledge to solve complex problems in industrial productions. An interdisciplinary curriculum for Intelligent Chemical Engineering (ICE) program was developed by a team consisting of professors in automation, chemical engineering and computer applications at Taiyuan University of Technology’s College of Chemical Engineering. This paper present how the interdisciplinary curriculum was implemented to the volunteer groups on trial for two years and the experimental classes of ICE for three years. Project-based learning (PBL) had been proved to be an effective means to implement the curriculum within limited teaching hours. Surveys for current students and graduates showed that the students’ ability of interdisciplinary thinking and dealing with complex problems, the skills to use professional software for digital plant design and smart plant management had been greatly improved.

Distance Education Experiences of Teacher-Parents during the COVID-19

This research aimed to determine the opinions of teacher parents about distance education process during COVID-19. The study was designed as a case study which is one of the methods in qualitative research. The sample of the study composed of 83 teacher parents from different branches in Turkey. Maximum variation and criterion sampling methods were used to select the participants. The data of the study were gathered though open ended questions developed by the researchers and were analyzed through descriptive and content analyses. According to the participants, distance education is perceived and accepted as a means of support rather than an alternative to face-to-face education. Participants mentioned the distance education carried out during the pandemic process as a beneficial practice in order to prevent students from breaking off from education but also they stated administrative issues, lack of computer science knowledge and internet-related problems. The inability to disseminate distance education to all students, especially disadvantaged students due to the lack of infrastructure, indifferent parents and the fact that distance education is insufficient in the education of young children/special education students show that distance education has not yet met the expectations to provide equal opportunities for everyone involved. Keywords: COVID-19, distance education, pandemic, teacher, parent

Migration of an Escape Room-Style Educational Game to an Online Environment: Design Thinking Methodology.

BackgroundThe COVID-19 pandemic outbreak has led to a sudden change in education, closing schools and shifting to online teaching, which has become an enormous challenge for teachers and students. Implementing adequate online pedagogical approaches and integrating different digital tools in the teaching process have become a priority in educational systems. Finding a way to keep students' interest and persistence in learning is an important issue that online education is facing. One possible way to establish engaging and interactive learning environments, using the energy and enthusiasm of students for educational purposes, is the use of game-based learning activities and gamification of different parts of the educational process.ObjectiveThis paper presents a use case of migrating an escape room–style educational game to an online environment by using the design thinking methodology. We wanted to show that the design thinking methodology is useful to create engaging and motivating online games that provide educational value.MethodsStarting from students’ perspective, we created a simple digital escape room–style game where students got an opportunity to self-assess their knowledge in computer science at their own pace. Students tested this prototype game, and their opinions about the game were collected through an online survey. The test's goal was to evaluate the students' perceptions about the implemented digital escape room–style educational game and gather information about whether it could achieve students' engagement in learning computer science during online teaching.ResultsIn total, 117 students from sixth and seventh grades completed the survey regarding the achieved student engagement. Despite the differences in students’ answers about game complexity and puzzle difficulty, most students liked the activity (mean 4.75, SD 0.67, on a scale from 1 to 5). They enjoyed the game, and they would like to participate in this kind of activity again (mean 4.74, SD 0.68). All (n=117, 100%) students found the digital escape room–style educational game interesting for playing and learning.ConclusionsThe results confirmed that digital escape room–style games could be used as an educational tool to engage students in the learning process and achieve learning outcomes. Furthermore, the design thinking methodology proved to be a useful tool in the process of adding novel educational value to the digital escape room–style game.

Consider “HACKS” when designing hackathon challenges: Hook, action, collaborative knowledge sharing

Our world’s complex challenges increase the need for those entering STEAM (Science, Technology, Engineering, Arts, and Math) disciplines to be able to creatively approach and collaboratively address wicked problems – complex problems with no “right” answer that span disciplines. Hackathons are environments that leverage problem-based learning practices so student teams can solve problems creatively and collaboratively by developing a solution to given challenges using engineering and computer science knowledge, skills, and abilities. The purpose of this paper is to offer a framework for interdisciplinary hackathon challenge development, as well as provide resources to aid interdisciplinary teams in better understanding the context and needs of a hackathon to evaluate and refine hackathon challenges. Three cohorts of interdisciplinary STEAM researchers were observed and interviewed as they collaboratively created a hackathon challenge incorporating all cohort-member disciplines for an online high school hackathon. The observation data and interview transcripts were analyzed using thematic analysis to distill the processes cohorts underwent and resources that were necessary for successfully creating a hackathon challenge. Through this research we found that the cohorts worked through four sequential stages as they collaborated to create a hackathon challenge. We detail the stages and offer them as a framework for future teams who seek to develop an interdisciplinary hackathon challenge. Additionally, we found that all cohorts lacked the knowledge and experience with hackathons to make fully informed decisions related to the challenge’s topic, scope, outcomes, etc. In response, this manuscript offers five hackathon quality considerations and three guiding principles for challenge developers to best meet the needs and goals of hackathon sponsors and participants.

Master's Degree in Health Data Science: Implementation and Assessment After Five Years.

Health data science is an emerging discipline that bridges computer science, statistics and health domain knowledge. This consists of taking advantage of the large volume of data, often complex, to extract information to improve decision-making. We have created a Master's degree in Health Data Science to meet the growing need for data scientists in companies and institutions. The training offers, over two years, courses covering computer science, mathematics and statistics, health and biology. With more than 60 professors and lecturers, a total of 835 hours of classes (not including the mandatory 5 months of internship per year), this curriculum has enrolled a total of 53 students today. The feedback from the students and alumni allowed us identifying new needs in terms of training, which may help us to adapt the program for the coming academic years. In particular, we will offer an additional module covering data management, from the edition of the clinical report form to the implementation of a data warehouse with an ETL process. Git and application lifecycle management will be included in programming courses or multidisciplinary projects.

ViviPaint: Creating Dynamic Painting with a Thermochromic Toolkit

New materials and technologies facilitate the design of thermochromic dynamic paintings. However, creating a thermochromic painting requires knowledge of electrical engineering and computer science, which is a barrier for artists and enthusiasts with non-technology backgrounds. Existing toolkits only support limited design space and fail to provide usable solutions for independent creation and for meeting the needs of the artists. We present ViviPaint, a toolkit that assists artists and enthusiasts in creating thermochromic paintings easily and conveniently. We summarized the pain points and challenges by observing a professional artist’s entire thermochromic painting creation process. We then designed ViviPaint consisting of a design tool and a set of hardware components. The design tool provides a GUI animation choreography interface, hardware assembly guidance, and assistance in assembly process. The hardware components comprise an augmented picture frame with a detachable structure and 24 temperature-changing units using Peltier elements. The results of our evaluation study (N = 8) indicate that our toolkit is easy to use and effectively assists users in creating thermochromic paintings.

Assessing the Level of Cybersecurity Knowledge of Computer Science and Applied Automation Undergraduates

The importance and role of cybersecurity awareness is more pressing than ever in today’s modern world and therefore, this concern gave voice to a number of studies evaluating the level of preparedness of individuals and professionals regarding cybersecurity aspects. Furthermore, the number of open positions on the job market in the field of cybersecurity highlight the topicality of the subject. In this paper, the author analyzed the level of knowledge that last year Computer Science and Applied Automation undergraduate students possess and also examined current practices around the world regarding cybersecurity education with the role of understanding the implications of cybersecurity education and proposing a set of measures and activities that can be applied to facilitate the educational process in the field of cybersecurity.

Studying Gender While ‘Studying Up’

This article discusses the epistemological challenges of contending with hegemonic norms while ‘studying up’. In particular, I discuss the challenges that I faced in following feminist and queer approaches to gender while conducting fieldwork on the gendered norms and values produced through undergraduate computer science education in Singapore. These approaches suggest critical questions about the construction of computer science knowledge and of the common focus on the ‘woman problem’ in computer science. While feminist and queer approaches enabled me to find partial connections with participants, the heteronormativity in/of computer science and problem-solving-based hegemonic epistemology brought challenges in maintaining my methodological and theoretical approach. I highlight the need for closer examination of the power dynamics and how these shape the epistemological risks of fieldwork while studying up.

Taxonomies of technological knowledge in higher education: A mapping of students’ perceptions

This paper presents the findings from a qualitative study exploring students’ perceptions of what constitutes technological knowledge. Technological knowledge dimensions from previous literature do not seem to be student-led, but rather suggested by the authors. It is therefore important to incorporate student views in order to create a more evidence-based taxonomy. Previous taxonomies of technological knowledge are also heavily linked to engineering-related disciplines, however definitions for use across the field of education and learning technologies would be helpful. In this study, a sample of student volunteers were interviewed about their understanding of technology enhanced learning and technological knowledge. The students were from a range of disciplines, not just engineering and science, so that technology knowledge for the general student population would be represented. An inductive thematic analysis was then carried out on the interview transcripts. Three knowledge types were derived from the thematic analysis: practical knowledge; structural knowledge; and computer science knowledge. These three empirically-derived technological dimensions were then mapped onto existing taxonomical structures from the literature. Finally, this paper discusses the implications of the student-generated dimensions for educators. Implications for practice or policy: Educators may need to consider how student-generated types of technological knowledge map onto existing technological knowledge structures and Bloom’s taxonomy. Educators can use the types of technology knowledge to target their teaching to their learners’ required knowledge level.

Pelatihan Design Sederhana Menggunakan Aplikasi Visual Studio 2010 dan SQL Server 2008 Pada Sekolah SMP IT Al Jabar

At this time, along with the development of the times, technology has also developed very rapidly, especially in the field of computers. Where can we know that the development of this technology can help human work. In the world of education, computer science is very necessary so that teachers or students do not lag behind in the development of today's times. For this reason, training in computer science is needed. Many of us know computers only limited to Ms. Office alone because it is often used in everyday life, but actually in computer science there are many applications/programs that can help human work to be easier. For this reason, this training was made to introduce one of the application programs in computer science, namely the application of visual studio 2010 and SQL server 2008. Where this application can create programs according to our wishes to help make our work easier. The object that will be used as the location of service is the teachers at the Al JABAR Middle School. In this service, the team wants to do it in the form of training and talk shows about Simple Design Training Using Visual Studio 2010 and SQL Server 2008 Applications at IT AL JABAR Junior High School. The results obtained at the IT Al-Jabar Junior High School from this activity are that teachers can increase their knowledge in computer science, especially in making simple designs using Visual Studio 2010 and SQL Server 2008 applications which are useful for teaching materials to their students

An Internet Articles Retrieval Agent Combined With Dynamic Associative Concept Maps to Implement Online Learning in an Artificial Intelligence Course

Online learning has been widely discussed in education research, and open educational resources have become an increasingly popular way to help learners acquire knowledge. However, these resources contain massive amounts of information, making it difficult for learners to identify Web articles that refer to computer science knowledge. This study developed an Internet articles retrieval agent combined with dynamic associative concept maps (DACMs). The system used text mining technology to analyze keywords to filter computer science articles. In previous research, concept maps were manually constructed; in this study, such maps can be automatically and dynamically generated in real time. In a case study of a fundamental course of artificial intelligence, this study designed two experiments to compare students’ learning behaviors while using this system and the Google search engine. The results of the first experiment showed that the experimental group searched for more knowledge articles on computer science using this agent, compared to the control group using the Google search engine. The learning performance of the experimental group was significantly better than that of the control group, while the cognitive load of the experimental group was significantly lower than that of the control group. Furthermore, the results of the second experiment showed that the learning progress of students using the agent was significantly greater than that of students who used the Google search engine. This illustrates that the agent effectively filtered computer science articles, and DACMs helped students gain a deeper understanding of academic concepts and knowledge related to artificial intelligence.

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study.

BackgroundThe amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty.ObjectiveWe developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed.MethodsThe methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs).ResultsFor the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents.ConclusionsWe proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

Text Classification in Organizational Research – A Hybrid Approach Combining Dictionary Content Analysis and Supervised Machine Learning Techniques

Big Data is an emerging field in organizational research as it provides new types of data, and technologies like digitization and web scraping allow to study huge amounts of data. Since large parts of digital data consist of unstructured text, text classification - assigning texts (or parts of texts) to predefined categories - is a central task. Text classification not only allows to identify relevant texts in a jumble of data but also to extract information from texts, such as sentiments, topics, and intentions. However, large amounts of textual data require the use of automated text mining methods, which is mostly uncharted territory in organizational research. We, therefore, outline and discuss the two existing approaches to text classification, one originating from social science (dictionary content analysis) the other from computer science (supervised machine learning). Since both approaches have advantages and disadvantages, we combine ideas from both to develop a hybrid approach that reduces existing issues and requires significantly less knowledge in programming and computer science than supervised machine learning. To illustrate our approach, we develop a classifier that identifies critical media coverage of organizational actions.

ЧИСЛОВЕ МОДЕЛЮВАННЯ ЯК ОДНА ІЗ СКЛАДОВИХ ФУНДАМЕНТАЛЬНОЇ МАТЕМАТИЧНОЇ ПІДГОТОВКИ ІНЖЕНЕРІВ

The article investigates the problem of mastering the skills of numerical modeling by students of engineering specialties while studying the course of higher mathematics. Much attention is paid to the formation of creative thinking of students in the process of learning mathematics. The main components of the methodological system using SCM in lectures, practical and individual classes. Using SCM MathCAD, Maple and others, the student can abstract from the technical details of programming, features of the operating system and focus on analyzing the features of such concepts as conditionality of the problem, stability of the method, evaluation of calculation results. Professional orientation of training future professionals with good personal qualities and is seen as creative, full of modern knowledge in mathematics and computer science. The given examples of tasks can be used for independent solution by students with a high cognitive level. Forming  of maintenance of course of higher mathematics is offered to carry out in such directions: strengthening  of role  of numerical  methods  and  their  realization after dopomogo of SKM; the use of mathematical design is during untiing of the applied tasks; capture students mathematical semantically professional by knowledges, necessary for the analytical  and numerical design of practical engineerings tasks, creation of own libraries of user of software products. Main abilities and skills which can be formed for students during computer-integrated employments after higher mathematics and elements of numerical design are selected: ability to use necessary programmatic facilities in the environment of SKM; ability to compare results in the different forms of presentation (analytical and graphic presentation); ability to use new possibilities, which are given computer technologies, based on the use of environment of SKM; ability to design programmatic facilities of the first in the environment of SKM; ability to design in the environment of SKM programmatic facilities of the first level of complication for untiing of educational and subject tasks.

An Novel Optimization Model for Ideological and Political Course in Colleges under the Environment of BD Mining Technology.

The big data (BD) era and pertinent computer science, pedagogy, and educational psychology knowledge are the foundations of this essay, based on the idea of the holistic development of human beings, the IPE (Ideological and Political Education) carrier theory, the new idea of educational development, and the strategy of strengthening the nation through education; the challenges IPECU (Ideological and political education in Colleges and Universities) faces and its path toward optimization are discussed relatively in detail in this paper, which conforms to the context of mega data thinking. The influence of IPECU's subject, object, and educational effect is discussed in this paper, along with tips for how to improve and innovate IPECU in the context of big data. In addition, it is noted that in the age of mega data, we must not only bravely take on challenges, but also actively seize opportunities if we are to realize the innovative and optimized development of IPECU. Moreover, the definitions of mega data and big data prediction function are provided in this paper. IPE can be used with the big data prediction function, which is further explored in this article. In order to realize the innovation of IPE mode in universities, this paper aims to encourage the integration and development of IPE and big data in academic settings.

Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning

Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions. We proposed a multi-energy level fusion model with a principal feature enhancement (PFE) block incorporating radiologist and computer science knowledge for Nmet prediction. The proposed model is custom-designed by gemstone spectral imaging (GSI) with different energy levels on dual-energy computer tomography (CT) from a primary tumor of lung cancer. In the experiment, we take three different energy level fusion datasets: lower energy level fusion (40, 50, 60, 70 keV), higher energy level fusion (110, 120, 130, 140 keV), and average energy level fusion (40, 70, 100, 140 keV). The proposed model is trained by lower energy level fusion that is 93% accurate and the value of Kappa is 86%. When we used the lower energy level images to train the fusion model, there has been a significant difference to other energy level fusion models. Hence, we apply 5-fold cross-validation, which is used to validate the performance result of the multi-keV model with different fusion datasets of energy level images in the pathology report. The cross-validation result also demonstrates that the model with the lower energy level dataset is more robust and suitable in predicting the Nmet of the primary tumor. The lower energy level shows more information of tumor angiogenesis or heterogeneity provided the proposed fusion model with a PFE block and channel attention blocks to predict Nmet from primary tumors.

Incidence of breast cancer and methylation of PCDH gene: a single ratio meta analysis of single rate

IntroductionBreast cancer presents one of the highest rates of prevalence. With the development of genetics and biotechnology, we have learned that the occurrence and development of many cancers are closely related to abnormal gene expression. At present, some pieces of literature have reported that there may be a correlation between the expression of PCDH and the occurrence of breast cancer.Therefore, we selected some loci from PCDH gene to explore the correlation between the methylation of PCDH gene and breast cancer.Material and methodsThis research is a systematic review and critical appraisal, make a meta-analysis of prospective and retrospective cohort study. Research was conducted through computer Science, Wanfang and Chinese knowledge network databases PubMed, Embase and Network. In a literature search, seven cohort studies were identified. This I2 statistic is used to quantify heterogeneity. A fixed effect model was used to synthesize the results. Regression tests of linear funnel plot asymmetry were used to estimate potential publication bias.ResultsThe methylation rate of PCDH gene in breast cancer with lymph node metastasis was 75%, and that in breast cancer without lymph node metastasis was 70%. The methylation rate of PCDH gene was 75% in breast cancer group with high expression of the Ki-67 gene and 71% in breast cancer group with low expression of Ki-67 gene.ConclusionsAccording to previous studies, the positive rate of methylation of PCDH gene in breast cancer tissues is higher than that in adjacent tissues, and there is no obvious statistical difference in the correlation between lymphatic metastasis and Ki-67.

Prediction of PM2.5 concentrations in Malaysia using machine learning techniques: a review

Particulate matter (PM), an air pollutant that is detrimental to breathing, is either emitted or formed ambiently. The exposure of respiratory system towards PM2.5, the fine particles of 2.5 micrometres diameter, causes complication for health. Thus, developing pollution control strategies requires the prediction of PM2.5concentrations. Advancement of technology and computer science knowledge, machine learning (ML) algorithms are used for highly accurate prediction of air pollutant concentrations. Recently, air quality in Smart Cities of Malaysia has been getting worse due to industrialization, emissions from private motor vehicles, and transboundary haze pollution. Therefore, the forecasting of PM2.5emissions to ensure they are within the statutory limits becomes necessary. Several machine learning methods have been implemented in existing research to predict air pollution concentrations in comparison to PM2.5. However, very few studies have used ML techniques to predict air quality in Malaysia when compared with global studies. Hence, to create awareness on the ML techniques and promote further research in this area, this study reviews and highlights most of the existing ML techniques for the prediction of PM2.5.