Reviewerly/exHarmony huggingface dataset:
Enhancing Prediction Precision and Reliability in Cervical, Lung, and Breast Cancer Diagnosis

Authors:
raw_author_name: Shivnath Ghosh
raw_affiliation_string: Brainware University, Barasat, India
raw_author_name: Avijit Kumar Chaudhuri
raw_affiliation_string: Brainware University, Barasat, India

Abstract: This study covers a wide range of cancers, focusing on cervical, lung, and breast cancer. Developing fast, accurate, and interpretable machine learning models for early diagnosis is critical to reducing the multifactorial mortality associated with these cancer types. Using a two-stage hybrid feature selection method, this study evaluates classification models using specific cervical, lung, and breast cancer data obtained from the UCI Machine Learning Repository. The cervical cancer dataset contains 36 features, the lung cancer dataset contains 16 features, and the breast cancer dataset contains 31 features. In the first stage, a random forest architecture is used for feature selection to identify features 5,7, and 7 that show a strong correlation with their cancer while reducing the difference between them. In Stage 2. Logistic regression (LR), naive bayes (NB), support vector machine (SVM), random forest (RF), and decision making (DT) were used to identify cervical cancer, lung, and breast cancer patients for five selections.
Managing and Monitoring Patient's Healthcare Using AI and IoT Technologies

Authors:
raw_author_name: Pankaj Bhambri
raw_affiliation_string: Guru Nanak Dev Engineering College, Ludhiana, India
raw_author_name: Alex Khang
raw_affiliation_string: Global Research Institute of Technology and Engineering, USA

Abstract: This book chapter explores the transformative integration of AI and IoT technologies in the realm of healthcare, focusing on their collective impact on patient monitoring. The chapter delves into the emerging trends and challenges associated with the utilization of AI and IoT in the digital healthcare landscape, emphasizing their role in real-time patient monitoring, data analytics, and personalized healthcare solutions. Through an examination of case studies and current applications, the chapter highlights the potential of these technologies to revolutionize patient care by enabling continuous and remote monitoring, early detection of health issues, and data-driven decision-making for healthcare professionals. Furthermore, the chapter addresses the ethical considerations and challenges that accompany the implementation of AI and IoT in healthcare settings. Overall, the chapter provides a comprehensive overview of the innovative ways AI and IoT technologies are reshaping patient monitoring practices, offering insights into the future of digital healthcare.
An Integrated Approach to Next-Generation Telemedicine and Health Advice Systems Through AI Applications in Disease Diagnosis

Authors:
raw_author_name: Rita Komalasari
raw_affiliation_string: Universitas Yarsi, Indonesia
raw_author_name: Alex Khang
raw_affiliation_string: Global Research Institute of Technology and Engineering, USA

Abstract: This chapter explores the transformative potential of integrating knowledge engineering and artificial intelligence in healthcare, focusing on constructing a knowledge-based clinical decision support system (KBCDS). The primary objective is to design an AI-enabled health portal to enhance accessibility to medical advice globally. The study investigates the application of AI in diagnosing COVID-19 and pneumonia, aiming to improve diagnostic accuracy and speed, reducing the burden on healthcare systems, and saving lives. The research is based on extensive literature study, delving into the depths of knowledge engineering, AI applications in healthcare, and medical ontology. The findings underscore the transformative potential of the integrated approach, highlighting its impact on healthcare disparities globally. The AI-enabled health portal proves to be a reliable source of medical advice, demonstrating that the fusion of knowledge engineering and AI technologies empowers medical professionals and significantly enhances healthcare accessibility and diagnostic capabilities.
Fuzzy Thresholding-Based Brain Image Segmentation Using Multi-Threshold Level Set Model

Authors:
raw_author_name: Alex Khang
raw_affiliation_string: Global Research Institute of Technology and Engineering, USA
raw_author_name: Avijit Kumar Chaudhuri
raw_affiliation_string: Brainware University, India

Abstract: Region of interest with reference to medical image is a challenging task. Clustering or grouping data objects can be used to isolate certain area of interest called image segmentation from human brain MRI scans is considered here. Together with the combination of Multilevel Otsu's thresholding and Level set approach, the most widely used fuzzy-based clustering like fuzzy C means (FCM) thresholding are taken into consideration. Here, the proper thresholding is determined using FCM thresholding. This threshold value can also be used to modify the Multilevel Otsu' method's threshold. The level set technique is then used to this segmented output image, yielding a more precise boundary level estimation. To improve brain MRI image segmentation, the proposed FTMLS system integrates the three aforementioned methods.
The Digital Competence of Students Preparing to become Primary School Teachers - Perspectives for Development

Authors:
raw_author_name: Vladimira Angelova
raw_affiliation_string: PPlovdiv University „Paisii Hilendarski“, bul. Bulgaria 236, Plovdiv, Bulgaria
raw_author_name: Aleksandra Nikolova
raw_affiliation_string: PPlovdiv University „Paisii Hilendarski“, bul. Bulgaria 236, Plovdiv, Bulgaria

Abstract: The analysis of the European Digital Competence Framework for Citizens, outlined the need to study: the digital competence of students - future primary teachers; their attitudes towards the study of required, elective and optional subjects developing digital competence. If students preparing to become primary teachers are offered sufficient and varied required, elective and optional subjects to develop these competences, they will be able to successfully apply digital competences in their future teaching practice. In May 2023, a survey was conducted with 200 students preparing to become primary teachers at the Pedagogical Faculty of Plovdiv University "Paisii Hilendarski", which would establish their attitudes on the issues raised. The results of this study showed: the students' high assessment of the usefulness of the studied compulsory disciplines for the development of digital competences, as well as an increased interest in the offered elective and optional disciplines with the same focus; the importance of primary teacher candidates having a good level of digital competences. The obtained results support the process of updating the current curricula in the disciplines developing digital competences and developing new training courses adapted to the needs of students for an even more complete development of their digital competences.
An Effective Technique to Detect WIFI Unauthorized Access using Deep Belief Network

Authors:
raw_author_name: William P.
raw_affiliation_string: Research and Development,  Department of Information Technology,  Sanjivani College of Engineering
raw_author_name: Azhagu Jaisudhan Pazhani A.
raw_affiliation_string: Department of Electronics &  Communication Engineering,  Ramco Institute of Technology

Abstract: Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.
Data Warehouse Design for Firefighters Operational at the DKI Jakarta Fire Department

Authors:
raw_author_name: Harco Leslie Hendric Spits Warnars
raw_affiliation_string: PComputer Science Department, BINUS Graduate Program - Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
raw_author_name: Arief Ramadhan
raw_affiliation_string: PJJ Informatika, Telkom University, Bandung, Indonesia 40257
raw_author_name: Teddy Siswanto
raw_affiliation_string: Information Systems Department, Trisakti University, Jakarta, Indonesia 11440
raw_author_name: Antoine Doucet
raw_affiliation_string: Laboratoire L3i - Université de La Rochelle, Avenue Michel Crépeau,  F-17 042 La Rochelle Cedex 1, France

Abstract: This paper proposed two models of data warehouse schema for the fire department of DKI Jakarta, where the 1st model contains six tables consisting of 3 fact and 3-dimensional tables, and the 2nd model only contains three fact tables. The 2nd model denormalises the 1st model, where the number of tables is less than the 1st model, where at the end of the day, the 2nd model will reduce the join table process, which increases the SQL performances. These two models have been recognised as fact constellation schema with more than one fact table and sharing dimension and sub-dimension tables. The database resources were collected from http://data.jakarta.go.id under the Fire and Rescue Service Agency. Those two data warehouse schema models were developed based on a report sector list, a report on Hydrants list, and vehicle register reports. This paper proposes to support Automatic Identification Systems (AIS) research, particularly implementing the data warehouse concept.
The Barriers and Readiness to Deal With Digital Transformation in Higher Education

Authors:
raw_author_name: Cucuk Wawan Budiyanto
raw_affiliation_string: Informatics Education, Universitas Sebelas Maret, Surakarta, Indonesia
raw_author_name: Rizka Latifah
raw_affiliation_string: Vocational Teacher Education,Universitas Sebelas Maret, Surakarta, Indonesia
raw_author_name: Herman Saputro
raw_affiliation_string: Mechanical Engineering Education, Universitas Sebelas Maret, Surakarta, Indonesia

Abstract: During the recovery from the COVID-19 pandemic, stakeholders in higher education institutions have been encouraged to adapt to the changes caused by digital transformation. This study employs a case study approach to investigate the barriers and readiness of students, educators, and higher education institutions for digital transformation. Data were collected through questionnaires and interviews with stakeholders in a higher education institution in Indonesia. The data from the questionnaire were analysed using qualitative descriptive analysis, while the transcripts of the interview data were run into the ATLAS.ti Qualitative Data Analysis application and analysed thematically. Codes developed from digital transformation barriers were suggested by Volgesang and later interpolated during data analysis into readiness and strategy codes. The emerging themes related to students and lecturers, and universities obstacles, readiness and strategies in dealing with digital transformation could then be identified. The findings are crucial for higher education institutions to thrive in the digitally evolving environment. Understanding how the university's stakeholders perceive the transformation would help anticipate a more knowledgeable and organized method to handle the intricacies and difficulties associated with such a profound change.
Survey Analysis of University Teachers in Ukraine Regarding the Use of Google Workspace for Education

Authors:
raw_author_name: Sergii Sharov
raw_affiliation_string: Department of Computer Sciences, Dmytro Motornyi Tavria State Agrotechnological University, 69600, Zaporizhzhia, Ukraine
raw_author_name: Serhii Tereshchuk
raw_affiliation_string: Department of Physics and Integrative Technologies of Teaching Natural Sciences, Pavlo Tychyna Uman State Pedagogical University, 20300, Uman, Ukraine
raw_author_name: Oksana Filatova
raw_affiliation_string: Department of Journalism, Advertising and PR Technologies, Admiral Makarov National University of Shipbuilding, 54000, Mykolaiv, Ukraine
raw_author_name: Olga Ksendzenko
raw_affiliation_string: Department of Physics and Integrative Technologies of Teaching Natural Sciences, Pavlo Tychyna Uman State Pedagogical University, 20300, Uman, Ukraine

Abstract: The article highlights the results of a survey of teachers at higher education institutions of Ukraine regarding the possibilities provided by Google Workspace for Education tools in the organization of the educational process. A questionnaire was used to gather data for this purpose, in which 167 respondents from different regions of the country took part. The questionnaire focused on the frequency of using specific educational applications, such as learning management systems, collaborative tools, and educational games. Most often, Google applications are used in blended learning (72% of respondents). To organize and conduct lectures/practical classes, teachers quite actively use Google Slides (79.4%/73.8% of respondents), Google Meet (69.2%/65.4% of respondents), YouTube (60.7%/56.1% of respondents). It was found that the vast majority of teachers (91%) positively evaluate the impact of Google Workspace for education tools on the effectiveness of teaching methods. In the future, it is planned to research students’ opinions regarding the use of Google Workspace for education while studying at university.
Quantum Computing in The Cloud - A Systematic Literature Review

Authors:
raw_author_name: Amirul Asyraf Zhahir
raw_affiliation_string: Universiti Sains Islam Malaysia, Faculty of Science and Technology, Bandar Baru Nilai, Negeri Sembilan, Malaysia
raw_author_name: Siti Munirah Mohd
raw_affiliation_string: Universiti Sains Islam Malaysia, Kolej PERMATA Insan, Bandar Baru Nilai, Negeri Sembilan, Malaysia
raw_author_name: Mohd Ilias M Shuhud
raw_affiliation_string: Universiti Sains Islam Malaysia, Faculty of Science and Technology, Bandar Baru Nilai, Negeri Sembilan, Malaysia
raw_author_name: Bahari Idrus
raw_affiliation_string: Universiti Kebangsaan Malaysia, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology Bangi, Selangor, Malaysia
raw_author_name: Hishamuddin Zainuddin
raw_affiliation_string: Xiamen University Malaysia, Department of Physics, School of Mathematics and Physics, Sepang, Selangor, Malaysia
raw_author_name: Nurhidaya Mohamad Jan
raw_affiliation_string: Universiti Sains Islam Malaysia, Kolej PERMATA Insan, Bandar Baru Nilai, Negeri Sembilan, Malaysia
raw_author_name: Mohamed Ridza Wahiddin
raw_affiliation_string: Universiti Sains Islam Malaysia, Tahmidi Centre, Bandar Baru Nilai, Negeri Sembilan, Malaysia

Abstract: Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.
Measuring digital skills in community adult learning settings – implications for Australian policy development

Authors:
raw_author_name: Amber Marshall
raw_affiliation_string: Griffith Business School, Department of Business Strategy &amp; Innovation, Griffith University, Nathan, Australia
raw_author_name: Kim Osman
raw_affiliation_string: School of Communication, Digital Media Research Centre, Queensland University of Technology, Brisbane, Australia
raw_author_name: Peta Mitchell
raw_affiliation_string: School of Communication, Digital Media Research Centre, Queensland University of Technology, Brisbane, Australia

Abstract: Social and economic participation is increasingly dependent on the proficient use of digital technologies in everyday life. Digital skills, alongside literacy and numeracy, are now foundational skills that all Australians should have. Yet, how policy makers, scholars, and adult educators can effectively identify people’s current and desired digital skills is not well established. Existing frameworks tend to be instrumentalist, listing generic abilities that all citizens need for success. Digital abilities, however, are context-specific and attempts to generalise need to be responsive to local circumstances. We present a theory-driven Digital Ability Self-Assessment Tool, developed for a government agency and piloted in a community organisation. The Tool takes a distinctive socio-material approach to overcome shortcomings of other digital ability assessment tools, which often focus on normative measures of skills attainment. Overall, we argue that authentic digital skills policy and programme development must account for the contexts in which digital technologies are used.
A Domain Adaptive Semantic Segmentation Method Using Contrastive Learning and Data Augmentation

Authors:
raw_author_name: Lihua Tian
raw_affiliation_string: School of Software Engineering, Xi’an Jiaotong University, Xi’an, China
raw_author_name: Chen Li
raw_affiliation_string: School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

Abstract: Abstract For semantic segmentation tasks, it is expensive to get pixel-level annotations on real images. Domain adaptation eliminates this process by transferring networks trained on synthetic images to real-world images. As one of the mainstream approaches to domain adaptation, most of the self-training based domain adaptive methods focus on how to select high confidence pseudo-labels, i.e., to obtain domain invariant knowledge indirectly. A more direct means to explicitly align the data of the source and target domains globally and locally is lacking. Meanwhile, the target features obtained by traditional self-training methods are relatively scattered and cannot be aggregated in a relatively compact space. We offer an approach that utilizes data augmentation and contrastive learning in this paper to perform more effective knowledge migration with the basis of self-training. Specifically, the style migration and image mixing modules are first introduced for data augmentation to cope with the problem of large domain gaps in the source and target domains. To assure the aggregation of features from the same class and the discriminability of features from other classes during the training process, we propose a multi-scale pixel-level contrastive learning module. What’s more, a cross-scale contrastive learning module is proposed to help each level of the model gain the capability to obtain more information on the basis of its own original task. Experiments show that our final trained model can effectively classify the images from target domain.
Improved Accuracy for Trotter Simulations Using Chebyshev Interpolation

Authors:
raw_author_name: Gumaro Rendon
raw_affiliation_string: Zapata Computing Inc., Boston, MA 02110, USA
raw_author_name: Jacob Watkins
raw_affiliation_string: Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI 48824, USA
raw_author_name: Nathan Wiebe
raw_affiliation_string: Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Pacific Northwest National Laboratory, Richland, WA 99352, USA

Abstract: Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.
Preventing Double Spending Attacks through Crow Search Algorithm to Enhance E-Voting System Security

Authors:
raw_author_name: A Kannammal
raw_affiliation_string: Coimbatore Institute of Technology

Abstract: Electronic voting system is the process of polling votes and counting votes. In most of the countries voting may now be done electronically, there are still several difficulties involved, including the expense of paper, how ballots are organized, the possibility of varying results when tallying the votes, and others. Duplicate votes pose a significant concern as they can be fraudulently cast by individuals. To focus on this issue, Distributed Ledger Technology (DLT) is employed to enhance the voting procedure in a secured manner. A directed acyclic graph is used by the Internet of Things Application (IOTA), a promising distributed ledger system. Faster transaction confirmation, high scalability and zero transaction fees are achieved via the Directed Acyclic Graph structure. In both IOTA tangle and blockchain technology, the public cast duplicate votes. The unauthorized user can create duplicate votes in the blockchain as well as IOTA tangle. This can be focused in this proposed method. The double spending problem can be solved by using Crow Search Algorithm (CSA). This Optimization problem produces an improved result for resolving double spending in e-voting systems.
A fast and efficient algorithm for time synchronization in satellite quantum key distribution

Authors:
raw_author_name: A V Miller
raw_affiliation_string: Moscow Centre for Quantum Technologies, Moscow, Russia

Abstract: Abstract Time synchronization is one of the most crucial issues that must be addressed in developing quantum key distribution (QKD) systems. It not only lets the transmitter and the receiver to assign a sequence number to each event and then do correct basis reconciliation, but also allows to increase signal-to-noise ratio. Time synchronization in satellite communications is especially complicated due to such factors as high loss, signal fading and Doppler effect. In this work, a simple, efficient and robust algorithm for time synchronization is proposed. It was tested during experiments on QKD between Micius, the world’s first quantum communications satellite, and an optical ground station located in Russia. The obtained synchronization precision lies in the range from 467 to 497 ps. The authors compare their algorithm for time synchronization with the previously used methods. The proposed approach can also be applied to terrestrial QKD systems.