Jurnal STAI Al-Hamidiyah
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Women's Representation at US Research Universities Across Disciplines and Professorial Ranks
No full textInstitutions of higher education in the United States increasingly rely on data-driven strategies to cultivate equitable learning environments and faculty cohorts. However, benchmarks for gender representation often fail to account for the distinct demographic norms of specific disciplines or the attrition rates observed at different stages of promotion. This project provides a transparent, national-level dataset of faculty gender ratios, broken down by academic discipline and professorial rank. By leveraging data from Academic Analytics, we offer a detailed cross-section of the current professoriate, moving beyond aggregate university-wide statistics to reveal field-specific disparities. These data tables, now accessible on the Open Science Framework (https://osf.io/uytf2/), serve as a critical resource for scholars and administrators seeking to contextualize local tenure and promotion trends against national disciplinary baselines
Exploring Cultural Differences in Retrospective Parental Bonding and Adult Attachment: A Cross-Cultural Study of Japan and China, Investigating the Mediating Role of Self-construal
No full textThis is a master's thesis research aiming at investigating the relationship between parental bonding and attachment, as well as the differences between culture
MMA - Multilayer Model of Automatization
No full textIn this project, Hermann Müller developed a model that simulates the emergence of automatic control in hierarchical multi-levels systems. Hermann Müller, Rouwen Canal-Bruland, Stefan Künzell and Torsten Schubert will publish a paper that relies on this model and demonstrates the theoretical assumptions and implications.
Please check the MMA manual (see folder below) to get further details and information on how to install and run the software
UAV test datasets
No full textThis project contains various datasets acquired using UAVs that can be used for testing methods for processing such datasets
Why Is Anything Conscious?
No full textWe tackle the problem of consciousness by taking the naturally selected, embodied organism as our starting point. We provide a formalism describing how biological systems such as human bodies self-organize to hierarchically interpret unlabelled sensory information according to valence. The system is attracted and repelled at different spatial and temporal scales. This is a qualitative interpretation of an unlabelled physical state. We show how such interpretations imply behavioural policies which are differentiated from each other only by this qualitative aspect of information processing. Natural selection favours systems that actively intervene in the world to achieve homeostatic and reproductive goals. Put provocatively, death grounds meaning. This means that in living systems information processing is necessarily subjective, that is, it has quality embedded into its very core. Qualitative information processing involves interoceptive and exteroceptive classifiers, and determines priorities for self-survival. We formulate The Psychophysical Principle of Causality as a theorem, and prove generalisation optimal learning forces this valence first ontology. Qualitative good or bad processing necessarily comes \textit{before} quality neutral representations of properties (i.e. ``red’’ is constructed from valence). Under selection pressures like sophisticated predation this produces a hierarchy of selves, of which reafference and reflective self awareness are a consequence. We discuss this in light of the seminal distinction between phenomenal and access consciousness. We claim that phenomenal consciousness without access is likely common, but the reverse is implausible. Our proposal lays the foundation of a formal science of consciousness, closer to human fact than zombie fiction
