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In this blog post, we would like to share some insights on our participation in the COMP90086 Computer Vision Project at the University of Melbourne (Semester 2, 2021).
In this blog post, I would like to present my submission to the COMP90042 Natural Language Processing Project at the University of Melbourne (Semester 1 2021).
In this blog post I would like to provide an anylsis of the Pima dataset that is available in the
faraway R-package. The exercises below are part of the course MAST90139: Statistical Modelling for Data Science at the University of Melbourne.
In this blog post I would like to provide an anylsis of the Swiss dataset which can be accessed in R. The exercises below are part of the course MAST90139: Statistical Modelling for Data Science at the University of Melbourne.
Over the past years, there have been major advancements in Artificial Intelligence and given the intense interest and investment in AI by industry and Academia, we believe that now is the time to focus our energies in applying AI to solve complex social problems in health, sustainability, community violence, and in assisting low resource communities.
A short announcements that I have now created my own alter ego in the form of a Twitter bot. It goes by the handle of @Bachfischer_bot and you can follow it on Twitter.
In a recent project that I did for my current employer (BCG Platinion), we were tasked with developing a digital Proof of Concept app that retrieves data from cars and analyzes it for interesting insights. The specifics of our work are of course under NDA, but nonetheless we were allowed to publish two short blog posts on LinkedIn that describe the great results that we have accomplished as a team.
Over the last couple of weeks, I published various articles on the concept of Business Managed IT on LinkedIn. Make sure to check them out if you would like to get the latest insights from practitioners in the industry and understand how Business Managed IT could be beneficial for your organization.
Last month, I had the pleasure to take part in the GermEval 2018 workshop on the identification of offensive comments in German language microposts. During my stay at KAUST, me and my supervisors took part in Task 1 (binary classification) of the shared task for German language classification.
From April to July 2018, I had the chance to work as a Visiting Student for the Laboratory Machine Intelligence and kNowledge Engineering (MINE) at King Abdullah University for Science and Technology (KAUST).
Published in SKILL 2018-Studierendenkonferenz Informatik, 2018
The terminology of Business-Managed IT refers to Shadow-IT systems which are operated overtly in the business units (BUs) and with the awareness from the IT department. They are a common phenomenon in corporations and usually emerge if the formal IT organization is unable to provide the BUs with solutions that meet their requirements. Because of this, Business-Managed IT is a highly significant area for an exploratory study, and both academia and practitioners can benefit from knowledge on how such systems can be successfully managed.
Recommended citation: Bachfischer, M. (2018). Success Factors in Business-Managed IT-A Case Study Analysis at a Large German Industrial Company. SKILL 2018-Studierendenkonferenz Informatik.
Published in 14th Conference on Natural Language Processing KONVENS 2018, 2018
In this paper, we present two deep-learning based classifier systems for the identification of offensive comments in German Language microposts: A bidirectional LSTM model and a CNN model. Our objective is to compare the performance of these two systems with a traditional, machine-learning based SVM classifier and to evaluate our approach on Task 1 (binary classification) of the GermEval 2018 shared task.
Recommended citation: Bachfischer, M., Akujuobi, U., & Zhang, X. (2018). KAUSTmine-Offensive Comment Classification on German Language Microposts. In 14th Conference on Natural Language Processing KONVENS 2018.
M.Sc. Thesis: Adversarial Workload Matters - Executing a Large-Scale Poisoning Attack against Learned Index Structures
Published in M.Sc. Thesis - The University of Melbourne, 2021
Databases rely on indexes to quickly locate and retrieve data that is stored on disks. While traditional database indexes use tree data structures such as B+ Trees to find the position of a given query key in the index, a learned index structure considers this problem as a prediction task and uses a machine learning model to “predict” the position of the query key. This novel approach of implementing database indexes has inspired a surge of recent research aimed at studying the effectiveness of learned index structures. However, while the main advantage of learned index structures is their ability to adjust to the data via their underlying ML model, this also carries the risk of exploitation by a malicious adversary.
Recommended citation: Bachfischer, M. (2021). Adversarial Workload Matters - Executing a Large-Scale Poisoning Attack against Learned Index Structures