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This paper reveals various approaches undertaken over more than two decades of teaching undergraduate programming classes at different Higher Education Institutions, in order to improve student activation and participation in class and consequently teaching and learning effectiveness.
While new technologies and the ubiquity of smartphones and internet access has brought new tools to the classroom and opened new didactic approaches, lessons learned from this personal long-term study show that neither technology itself nor any single new and often hyped didactic approach ensured sustained improvement of student activation. Rather it needs an integrated yet open approach towards a participative learning space supported but not created by new tools, technology and innovative teaching methods.
This Article introduces two research projects towards assistive robotic arms for people with severe body impairments. Both projects aim to develop new control and interaction designs to promote accessibility and a better performance for people with functional losses in all four extremities, e.g. due to quadriplegic or multiple sclerosis. The project MobILe concentrates on using a robotic arm as drinking aid and controlling it with smart glasses, eye-tracking and augmented reality. A user oriented development process with participatory methods were pursued which brought new knowledge about the life and care situation of the future target group and the requirements a robotic drinking aid needs to meet. As a consequence the new project DoF-Adaptiv follows an even more participatory approach, including the future target group, their family and professional caregivers from the beginning into decision making and development processes within the project. DoF-Adaptiv aims to simplify the control modalities of assistive robotic arms to enhance the usability of the robotic arm for activities of daily living. lo decide on exemplary activities, like eating or open a door, the future target group, their family and professional caregivers are included in the decision making process. Furthermore all relevant stakeholders will be included in the investigation of ethical, legal and social implications as well as the identification of potential risks. This article will show the importance of the participatory design for the development and research process in MobILe and DoF-Adaptiv.
The DECIMER.ai Project
(2024)
Over the past few decades, the number of publications describing chemical structures and their metadata has increased significantly. Chemists have published the majority of this information as bitmap images along with other important information as human-readable text in printed literature and have never been retained and preserved in publicly available databases as machine-readable formats. Manually extracting such data from printed literature is error-prone, time-consuming, and tedious. The recognition and translation of images of chemical structures from printed literature into machine-readable format is known as Optical Chemical Structure Recognition (OCSR). In recent years, deep-learning-based OCSR tools have become increasingly popular. While many of these tools claim to be highly accurate, they are either unavailable to the public or proprietary. Meanwhile, the available open-source tools are significantly time-consuming to set up. Furthermore, none of these offers an end-to-end workflow capable of detecting chemical structures, segmenting them, classifying them, and translating them into machine-readable formats.
To address this issue, we present the DECIMER.ai project, an open-source platform that provides an integrated solution for identifying, segmenting, and recognizing chemical structure depictions within the scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilizes a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string.
A key strength of DECIMER.ai is that its algorithms are data-driven, relying solely on the training data to make accurate predictions without any hand-coded rules or assumptions. By offering this comprehensive, open-source, and transparent pipeline, DECIMER.ai enables automated extraction and representation of chemical data from unstructured publications, facilitating applications in chemoinformatics and drug discovery.
Purpose
So far, there are several approaches of measuring the Dark Triad traits, but still all of them are
personality questionnaires with at least questionable usability for applied contexts such as Human
Resource Management.
The purpose of the study is the development of a structured interview with the aim of measuring the Dark Triad in a rather qualitative way that increases social validity for the respondents.
Design/Methodology/Approach/Intervention
In the present study, 15 executives from the telecommunications industry were interviewed on their personal evaluation of management success and derailment. Afterwards, their personality traits of the Dark Triad were measured with the help of the Short Dark Triad Scale. Subsequently, the data from qualitative and quantitative research were examined for correlations using the mixed-method approach.
Results
The results of the mixed-method approach showed a statistically significant correlation between the Short Dark Triad Scale and the ratings for narcissism, Machiavellianism and subclinical psychopathy in the Dark Triad interview.
Limitations
Replicating the results in a bigger sample and a deeper investigation of the criterion-related validity as well as an integration of multiple raters can provide more confidence in our results.
Research/Practical Implications
Structured interviews allow the measurement of personality traits in a more convenient way especially in personnel selection and development processes. Identifying subclinical traits in leadership candidates can, e.g. prevent management derailment.
Originality/Value
The present study advances the measurement methods of the Dark Triad.
Purpose
Although the systemic approach to the leadership concept seems to fit well into our modern complex and dynamic work environment, only little research has been conducted to define and assess systemic leadership. In this study we therefore developed and assessed criterion validity of the
multidimensional systemic leadership inventory (SLI, Sülzenbrück & Externbrink, 2017).
Methodology
We conducted two cross-sectional survey among managers and employees of various organizations (N = 143 and N = 150).
Results
We found a robust five-factor structure of the SLI, comprising systemic thinking, self-knowledge, solution-oriented communication, creating meaning and delegation. Regarding criterion validity, a significant positive correlation of systemic leadership was found with affective commitment, while a significant negative correlation with emotional strain in occupational contexts occurred. These overall positive outcomes for employees were not undermined by negative personality traits of the employee (Machiavellianism), while strong growth need strength further enhanced positive effects on affective commitment.
Limitations
Since all variables were measured as self-reports, common method variance could limit our findings.
Practical Implications
Systemic leadership is a very promising new approach for leaders to ensure committed and less strained employees.
Value
Systemic leadership, especially in terms of a leaders’ understanding of organizational and private systems influencing work behaviour of all members of an organization, is a promising novel leadership model suitable to address challenges of complex and dynamic work environments.
Neuroscientists want to inspect the data their simulations are producing while these are still running. This will on the one hand save them time waiting for results and therefore insight. On the other, it will allow for more efficient use of CPU time if the simulations are being run on supercomputers. If they had access to the data being generated, neuroscientists could monitor it and take counter-actions, e.g., parameter adjustments, should the simulation deviate too much from in-vivo observations or get stuck.
As a first step toward this goal, we devise an in situ pipeline tailored to the neuroscientific use case. It is capable of recording and transferring simulation data to an analysis/visualization process, while the simulation is still running. The developed libraries are made publicly available as open source projects. We provide a proof-of-concept integration, coupling the neuronal simulator NEST to basic 2D and 3D visualization.
Steps Towards an Open All-in-one Rich-Client Environment for Particle-Based Mesoscopic Simulation
(2018)
This technical report is about the architecture and integration of very small commercial UAVs (< 40 cm diagonal) in indoor Search and Rescue missions. One UAV is manually controlled by only one single human operator delivering live video streams and image series for later 3D scene modelling and inspection. In order to assist the operator who has to simultaneously observe the environment and navigate through it we use multiple deep neural networks to provide guided autonomy, automatic object detection and classification and local 3D scene modelling. Our methods help to reduce the cognitive load of the operator. We describe a framework for quick integration of new methods from the field of Deep Learning, enabling for rapid evaluation in real scenarios, including the interaction of methods.