Curriculum guidelines for new Fintech Master's Programmes

Assalamu’alaikum Wr. Wb. and Greetings.I am pleased and honored to welcome you at the First International Conference on Computer Science and Applied Mathematic (ICCSAM 2018), which was held this year by AMIK and STIKOM Tunas Bangsa Pematangsiantar in collaboration with the Indonesian Computer and Information Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS) and Bank Muamalat. As Chair of the Foundation, I would like to convey a greeting Welcome to Parapat City Lake Toba to presenters from various countries and especially those I respect for the presence of scientists in the field of Computer and Mathematical Sciences and industry practitioners in the 2018 ICCSAM event.I see that this activity is designed to enhance the exchange of knowledge and new discoveries in computer science and mathematics and related fields in industry. I hope that scientists in the field of computer science and mathematics and those who work in the industry can share knowledge and work together as a team that has mutual relations with each other. I am very pleased to say that the theme of this conference, “Advancing Computability Innovation” is very much in line with the objectives of AMIK and STIKOM conferences and missions, namely “to become a Science and Technology based Study Program that meets industrial needs and functions as a research center in Information Data Science. The conference was held in response to output and aoutcame which had a significant contribution in the field of computer science and mathematics as an inseparable unit of science towards the development of local and global industries. It is our happiness and honor to welcome distinguished professors present to convey their expertise at this conference.I hope this meeting will enable the development of productive dialogue between participants from various countries. They also provide invaluable opportunities for networking among participants, institutions and industries. I also hope that diversity in these fields can reveal more opportunities for researchers and practitioners from all over the world to start a lot of research related to the industry in the future.I would like to congratulate AMIK and STIKOM Tunas Bangsa for starting this conference with a synergistic contribution from dedicated partners namely the USU Faculty of Mathematics and Natural Sciences, USU’s Faculty of Computer and Information Technology, the Indonesian Computer and Informatics Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS), Bank Muamalat, Malaysia Pahang University (UMP), University of Essex, United Kingdom, Ankara, Turkey and Institute of Applied Mathematics Middle East Technical University to all delegates for their full support, cooperation and contribution to the 2018 ICCSAM. I also want to thank the Organizing Committee for their perseverance and extraordinary efforts. Various sponsors are also rewarded for their contributions. I really hope that all participants will get a pleasant stay here at NIAGARA HOTEL and bring back unforgettable experiences and valuable knowledge from this conference.Thanks.H. Ahmad Ridwansyah Putra

To keep up with the dynamics of nowadays researches and technologies, study programmes - particularly master programmes - need to continuously evolve. Additionally, rigid regulations drive the structure and content of such programmes. Adding competitiveness to these as a must, one can easily infer that building (or updating) a study programme is not an easy task. Moreover, expectancies and requirements from industry might even interfere with specific rules that study programmes have to be compliant to. This paper introduces an approach to overpass the challenges above, by employing the TRIZ-M framework. Problem definition, needs, performances, constraints, technical conflicts and innovation vectors are the key ingredients of the framework that has been used to support the development of a competitive master study programme in Robotics at the Technical University of Cluj-Napoca.

Despite great effort in research and teaching as well as in raising women’s quotas in the public sector and in enterprises, a gender gap in computer science can still be observed. The reasons are manifold, including the lack of interest of girls because of existing stereotypes of male nerds, misconceptions in the field of computer science, and concrete differences in students’ self-concept and performance. Girls do not choose schools or studies in the field of computer science because working on computers or coding is unattractive to them. Research shows poorer performance, missing interest, and lower self-concept for girls in secondary computing education as well as a high dropout rate of female students in computer science courses at the bachelor’s level. Software development exams contribute to those high dropout rates, and women often choose a different subject or even stop their university education altogether. This leads to fewer female employees in the field of computer science, and this situation calls for special measures at several levels. The current paper provides an overview of the initiatives of our university that aim to address several aspects of the gender gap. The programs offered aim to (1) increase interest in computer science and recruit (highly) gifted girls for our talent programs, (2) recruit and support female bachelor’s and master’s students in computer science, and (3) redesign programming courses and teaching materials to reduce the gender gap in performance and support, especially for female students. This study also describes a cyber tutoring program in which highly gifted girls between 13 and 16 years collaborate with and are supervised by female role models in higher positions in STEM. The evaluation results gained in the first year of this case study are promising and suggest that the extension and further development of the program would be advantageous.

Artificial intelligence (AI) is a rapidly evolving field that has seen tremendous growth in recent years. In this chapter, the authors provide an overview of current trends in AI and their applications in computer science. They also discuss the future directions of AI research and their potential impact on the field of computer science. They start by introducing the basic concepts of AI and its various subfields. Then, they present an overview of current trends in AI research, including machine learning, natural language processing, computer vision, and robotics. The authors discuss how these trends are applied in computer science, such as autonomous vehicles, fraud detection, and personalized medicine. Finally, they discuss the future directions of AI research, including the development of more explainable AI systems, the integration of AI with other emerging technologies, and the ethical considerations of AI.

The article presents real experience with integration of "Bologna declaration" into study programs in the field of computer science on University of Ostrava. The designed model inheres not only professional CS studies but also teacher studies at all levels of university studies.

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

Using quantum computing algorithms in future satellite communication

Welcome to the proceedings of the 2023 International Conference on Advances in Computer Science and Engineering Technology (ACSE 2023), held from July 29 to 30 in Guangzhou, China. These proceedings are a compilation of the valuable research contributions and innovative ideas presented during the conference, showcasing the latest advancements in the fields of computer science and engineering technology.As we navigate the complexities of the modern world, computer science and engineering technology have emerged as indispensable tools in addressing challenges and creating new possibilities. Our collective efforts, expertise, and willingness to embrace change are vital in harnessing the potential of these transformative fields. ACSE 2023 provided a unique platform for researchers, academicians, professionals, and industry experts to come together and exchange their insights, experiences, and discoveries. The conference aimed to foster interdisciplinary discussions, explore emerging trends, and promote collaboration among individuals from various domains within the realm of computer science and engineering technology.This volume features a diverse collection of papers covering a wide range of topics, including but not limited to Artificial Intelligence, Machine Learning, Engineering and Simulation, Optimization, Control, and Algorithms and more. The contributions presented here reflect the dedication and enthusiasm of the research community in addressing the complex challenges and opportunities that our rapidly evolving technological landscape presents.The peer-reviewed papers presented in these proceedings undergo a rigorous evaluation process, ensuring the quality, originality, and significance of the research work. Each paper represents the author’s commitment to advancing their respective fields and contributing to the global body of knowledge.We extend our sincere gratitude to the conference organizers, the program committee, the reviewers, and all the authors who have contributed to these proceedings. Their hard work and dedication have been instrumental in making ACSE 2023 a success and enriching the academic discourse in the fields of computer science and engineering technology. Special thanks to the members of the Journal of Physics: Conference Series for making this Proceedings published, which ensures that the valuable insights shared during the conference will reach a broader audience and have a lasting impact on the advancement of science and technology.Once again, we welcome you to the proceedings of the 2023 International Conference on Advances in Computer Science and Engineering Technology and hope that this collection of research papers will both inform and inspire you in your own academic and professional pursuits. ACSE 2023 Organizing Committee List of Committee Members are available in this pdf.

Quantum computing has quickly become at the vanguard of many computer science fields today. This is motivated by quantum computers ability to perform computational tasks with efficiencies that are not possible with classical computers. Quantum computing technology has a lasting potential impact on society and a broad implications on many aspects of our lives. Communications Industry Researchers (CIR) estimates that the global quantum-computing market is expected to reach $1.9 billion in 2023, increasing to $8 billion by 2027, in spending on associated software and services. Although quantum computing has several advantages, it threatens the current cryptographic techniques, which are necessary for today’s Internet standards like the Transport Layer Security (TLS). This is due to its ability to perform infinite complex computations in parallel. Therefore, it is expected that quantum computers will be able to solve the key distribution problem and break every single cryptography and authentication algorithm based on Asymmetric cryptography, such as: RSA, ECC, and Diffie-Hellman. New emerging technologies such as the Internet of Things (IoT), blockchain, autonomous vehicles, 5G, Artificial Intelligence (AI) and robotics all rely on cryptography for their secure operation. Although quantum computing supports technological evolution and growth, it will pose a threat to the current progress and future development of these technologies. Therefore, cryptography and authentication techniques need improvements as introducing quantum computing would put today’s cybersecurity in serious risk. NSA and NIST proposed solutions to tackle these challenges. However, these solutions are still immature as they need major upgrade of hardware implementation. Instead, the recommendations focus on what is called post-quantum, quantum-proof, quantum-safe or quantum-resistant cryptosystems. This talk outlines how the impact of quantum technologies on authentication, data exchange, and data storage, as well as areas where cryptography plays an essential role, such as: blockchain, 5G, IoT, and AI technologies. It investigate the possible impact of quantum technologies on these emerging technologies. Finally, it discusses some of the current quantum security solution that are devoted to protect the devices and systems in the quantum era, and mitigate quantum attacks.

This Research to Practice Full Paper discusses an approach to motivating graduate computer science and electrical engineering students in the study of quantum computing. The U.S. Congress and other governmental and industrial organizations have recognized that teaching Quantum Computing in university-level Computer Science and Electrical Engineering programs is strategically important for workforce development along with technology development. Success in this endeavor requires generating genuine interest in the topic and motivating potential students. Quantum Computing requires an understanding of relatively difficult related subjects including physics, math and computer science. Many computer science and electrical engineering students shy away from quantum computing because of its complexity. This paper discusses our success with generating interest in quantum computing among graduate students and motivating them through exploring, in related courses, some aspects of quantum computing that have the potential to generate discontinuities. The approach is also examined in terms of the Scientific Learning Cycle as discussed by Wankat & Oreovicz. Opportunities and challenges of adding Quantum Computing to Computer Science and Electrical Engineering programs were studied through a student small group project in a cybersecurity course. The work built on extensive research into the effectiveness of small group projects and the role of “play” in learning. Our small group project on quantum computing was introduced three years ago into a cybersecurity course which is required for all students in our university's Masters in Computer Science and Masters in Electrical Engineering programs. The basic goals of the project are to teach students some basics of quantum computing by allowing them to “play with it” and to alert students to the fact that quantum computing is a rapidly developing new field that they need to follow during at least the next decade. The small group project helped students to explore the potential of quantum computing to substantially undermine current Public Key Cryptography and, at the same time provide a new approach to encryption that is even more secure than Public Key Cryptography. A qualitative analysis of discussions and assigned tasks suggests that this strategy succeeded in generating significant interest in quantum computing among students. They became motivated and immersed in a difficult subject despite the requirement for investment of significant time and effort to understand a complex combination of physics, math and computer science.

A Busy March Meeting Is Brewing in Seattle

In this era and in the future, artificially intelligent machines are replacing and playing a key role to enhance human capabilities in many areas. It is also making life style better by providing convenience to all including normal human beings and professionals as well. That is why AI is gaining huge attention and popularity in the field of computer science by which it has revolutionized the rapidly growing technology known as expert system. The applications of AI are working in many areas with huge impact and being used widely as well. AI provides quality and efficiency in almost every area, we are evolving it in. The main purpose of this paper is to explore the area of medical and health-care with respect to AI along with ‘Machine Learning’, and ‘Neural Networks’. This work explores the current use of AI in innovations, in the particular field of Bio-Medical and evaluated that how it has improved hospital inpatient care and other sectors related to it i.e. smart medical home, virtual presence of doctors and patients, automation in diagnostic, etc. that has changed the infrastructure of medical domain. Finally, an investigation of some expert systems and applications is made. These systems and applications are widely used throughout the world and a ranking mechanism of their performance has proposed accordingly in an organized manner. We hope, this work will be helpful for the researchers coming to this particular area and to provide a syntactic information that how computer science (i.e. AI, ANN, ML) is revolutionizing the field of bio-medical and healthcare.

We present our process and development of a web-based system to explore the publication networks of faculty in California public universities in the fields of computer science and electrical engineering. Our project explores collaboration networks in the fields of computer science and electrical engineering with a focus on publication networks and an analysis of these collaborations with a focus on geospatial organization (which institutions are collaborating with which other institutions). We present our data gathering process, which relies on the Scopus[9] database (Scopus represents a “comprehensive overview of the world’s scientific research output across all disciplines”), and we present the development of a web-based tool using python and the Google Maps API [12] in order to allow visualizations and explorations of geospatial structures of the publications networks. These visualizations drove further network analysis, which we present here as well.

Quantum computing is a multidisciplinary field at the intersectionof quantum physics, computer science, and mathematics, aiming to har-ness quantum mechanical phenomena for computational advantage. Thisarticle provides a comprehensive review of both the foundational princi-ples and advanced theoretical models of quantum computing. We beginwith the basic framework of quantum computation, introducing the con-cept of qubits as two-level quantum systems, the linear algebra of Hilbertspaces, Dirac’s bra–ket notation, and the postulates of quantum mechan-ics (state superposition, unitary evolution, and projective measurement).We then discuss quantum logic gates and circuits, highlighting how clas-sical reversible computation principles extend to universal quantum gatesets. Next, we survey key quantum algorithms (from Shor’s factoring toGrover’s searching and beyond) and the complexity-theoretic implicationsof quantum computers, contrasting the class BQP with classical complex-ity classes. We cover the theoretical foundations of quantum error cor-rection and fault tolerance, explaining how quantum information can beprotected from decoherence using redundancy and how fault-tolerant pro-tocols can, in principle, allow scalable quantum computation despite noisyhardware. We then explore advanced models and paradigms of quantumcomputation, including measurement-based quantum computing (one-wayquantum computing), topological quantum computation with anyons, adi-abatic quantum computing and quantum annealing, continuous-variablequantum computing, and related approaches. We also provide an overviewof quantum information theory as it relates to computation, discussingentanglement, quantum entropy, and information-theoretic limits like theHolevo bound. Finally, we review the leading physical implementations(quantum hardware models) of quantum computers—trapped ions, super-conducting circuits, photonic systems, solid-state spin qubits, and oth-ers—outlining the challenges and achievements of each approach.

For more than two decades, University Aalborg has enrolled learners into their part-time engineering Master's programmes. These programmes are designed for employed learners who work full-time in the industry and who want to keep their job while they study on a Master's programme. Aalborg University's pedagogical approach is solely based on problem-based learning, which means that learners are, to a large extent, implementing real-life engineering problems as a means of learning. This paper will investigate what types of problem formations the learners are able to identify from their working-life, which is also aligned with the requirements of the part-time master's study programme.