Unpacking the ‘black box’: the importance of process data to explain outcomes

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

Download Full-text

Sensors with Digital Output – A Metrological Challenge

19th International Congress of Metrology (CIM2019) ◽

10.1051/metrology/201922002 ◽

2019 ◽

Cited By ~ 2

Author(s):

Michael Mende ◽

Philipp Begoff

Keyword(s):

Signal Processing ◽

Internet Of Things ◽

Measurement Uncertainty ◽

Black Box ◽

The Internet ◽

Process Data ◽

Digital Output ◽

Calibration Standards ◽

New Applications ◽

The Internet Of Things

The Internet of Things (IoT) as well as many other new applications require sensors that can already process data inside the sensor and exchange the pre-processed data more or less directly with their environment. Such sensors typically have a digital output and thus challenge current calibration systems which usually have analogue input channels. Furthermore most calibration standards were written for an analogue world and do not fit to sensors with internal A/D converters and data pre-processing. Based on experiences of the authors with the calibration of accelerometers with digital output, the paper will give an overview over the challenges that we will face in a digital sensor world. How will calibration systems for such transducers will look like? How do I calculate a measurement uncertainty if the signal processing inside a sensor is a black box? The paper addresses the challenges and tries to give an outlook how to meet them.

Download Full-text

Call for Papers: “Process Data in Computer-Based Assessment: Opening the Black Box”

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000658 ◽

2021 ◽

Vol 37 (2) ◽

pp. 161-162

Keyword(s):

Black Box ◽

Process Data ◽

Computer Based

Download Full-text

Engaging Undergraduate Students in Child Language Research

Perspectives on Issues in Higher Education ◽

10.1044/aihe17.1.4 ◽

2014 ◽

Vol 17 (1) ◽

pp. 4-16

Author(s):

Jade H. Coston ◽

Corine Myers-Jennings

Keyword(s):

Undergraduate Students ◽

Student Growth ◽

Process Data ◽

Research Knowledge ◽

Research Dissemination ◽

Language Sampling ◽

Language Research ◽

Language Sample Analysis ◽

Turn Taking ◽

Assessment Techniques

To better prepare the professionals and scholars of tomorrow in the field of communication sciences and disorders (CSD), a research project in which undergraduate students collected and analyzed language samples of child-parent dyads is presented. Student researchers gained broad and discipline-specific inquiry skills related to the ethical conduct of research, the literature review process, data collection using language assessment techniques, language sample analysis, and research dissemination. Undergraduate students majoring in CSD developed clinical research knowledge, skills, and dispositions necessary for future graduate level study and professional employment. In addition to the benefits of student growth and development, language samples collected through this project are helping to answer research questions regarding communicative turn-taking opportunities within the everyday routines of young children, the effects of turn-taking interactions on language development, and the construct validity of language sampling analysis techniques.

Download Full-text