Invited Speakers

 

 

Assoc. Prof. Pyshkin Evgeny, University of Aizu, Japan

 

 

Evgeny Pyshkin received his Ph.D. in Computer Science from Peter the Great St. Petersburg Polytechnic University in 2000. He is currently a senior associate professor with the School of Computer Science and Engineering of the University of Aizu. His majors are in the areas of software engineering, software education, and human-centric computing. Inspired by digital transformation challenges, Prof. Pyshkin participated in multi-disciplinary projects aimed at improving information technology applications for individuals and society, which include information services for travelers, software testing framework for mobile development, prosody-based personalized computer-assisted language learning systems, and computational models for music. He is an author of several books on software design and programming in C, C++, Java and C#. Besides his majors, he is enthusiast of research on cross-cultural communication and bridging technology disciplines to art and humanities. In scope of this activity, he delivered a number of invited talks on music, architecture, and language research.

 

Title: “Tailored Fit”: Improving Computer-Assisted Pronunciation Training Tools Feedback to Language Learners

 

Abstract: This talk contributes to the discourse on computer-assisted language learning tools in scope of human-centric computing agenda. Our particular focus is on the pronunciation training tools and possible approaches to improve the features that would provide a meaningful feedback to language learners. Using our own project on developing a computer-assisted prosody training environment, we will discuss a number of necessary algorithms and technology and interface solutions for speech processing, visualization and evaluation that can contribute to the production of better tailored instructive feedback combining audial, visual and calculable components.

 

 

 

  Assoc. Prof. Bambang Leo Handoko, Bina Nusantara University, Indonesia

 

Bio: Associate Professor Bambang Leo Handoko, academics and practitioners in the field of Auditing. Experience as auditor in public accounting firm, internal auditor for corporations and auditor for securing vital objects of the National Police Headquarters. He is an expert in financial auditing, forensic accounting, information technology auditing and also e-business. He has had many international publications in reputable journals and proceedings with many citation and acknowledgement from international researchers. He had won a lot of research grant from institution and government. Currently work as Subject Content Coordinator Auditing in Accounting Department, Faculty of Economic and Communication, Bina Nusantara University of Indonesia. He also technical committee in many reputable journal publisher and earn Scopus hi Index.

 

Title: Planned Behavior and Social Cognitive Model for Accounting Student Intention in Learning Audit Software

 

Abstract: The industrial revolution 4.0 has penetrated all fields, including accounting and auditing. Higher education institutions and universities, in this case as labor printers, are also required to prepare graduates who are technologically savvy. We at the auditing scientific community also prepare students to become auditors who understand technology, in this case audit software. In order to become quality auditor in the future, literacy in audit software is mandatory for accounting student. We examine the factors that influence student interest in learning audio software. We use the approach of the theory of planned behavior and social cognitive theory. Our research is quantitative research, the object of our research is students who take the method and practice of computerized audit course. The data we process is primary data from questionnaires to respondents. We use statistical software for data analysis, namely Smart PLS 3. Our results state that the variables attitude, perceived behavioral control and self-efficacy have a significant effect, while subjective norms have no significant effect on student intention in learning audit software.

 

 

 

Dr. Sam Leewis, HU University of Applied Sciences Utrecht, Netherlandsss

 

Title: Decision Mining Theory and Practice: Leveraging Structured Data for the Improvement of Decision-making

Abstract: Operational decision-making with high volume decisions in products and services result into sets of data without any added value or valuable insights. This (structured) data can be used to learn more about these decisions and possible related decisions. The more often a decision is made, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. Decision mining can be segmented into three phases: Discovery, Conformance Checking, and Improvement. This talk will focus on the added value of Decision Mining through examples from research and practice and concludes with a research agenda for further development of Decision Mining.