Assessing the utility of text-to-SQL approaches for satisfying software developer information needs. - In: Empirical software engineering, ISSN 1573-7616, Bd. 29 (2024), 1, 15, S. 1-48
Software analytics integrated with complex databases can deliver project intelligence into the hands of software engineering (SE) experts for satisfying their information needs. A new and promising machine learning technique known as text-to-SQL automatically extracts information for users of complex databases without the need to fully understand the database structure nor the accompanying query language. Users pose their request as so-called natural language utterance, i.e., question. Our goal was evaluating the performance and applicability of text-to-SQL approaches on data derived from tools typically used in the workflow of software engineers for satisfying their information needs. We carefully selected and discussed five seminal as well as state-of-the-art text-to-SQL approaches and conducted a comparative assessment using the large-scale, cross-domain Spider dataset and the SE domain-specific SEOSS-Queries dataset. Furthermore, we study via a survey how SE professionals perform in satisfying their information needs and how they perceive text-to-SQL approaches. For the best performing approach, we observe a high accuracy of 94% in query prediction when training specifically on SE data. This accuracy is almost independent of the query’s complexity. At the same time, we observe that SE professionals have substantial deficits in satisfying their information needs directly via SQL queries. Furthermore, SE professionals are open for utilizing text-to-SQL approaches in their daily work, considering them less time-consuming and helpful. We conclude that state-of-the-art text-to-SQL approaches are applicable in SE practice for day-to-day information needs.
https://doi.org/10.1007/s10664-023-10374-z
The framing of diversity statements in European universities: the role of imprinting and institutional legacy. - In: Minerva, ISSN 1573-1871, Bd. 62 (2024), 1, S. 69-92
We analyze the role of institutional founding conditions and institutional legacy for universities’ self-representation in terms of diversity. Based on 374 universities located in the Czech Republic, France, Germany, Hungary, Italy, and Poland, we can differentiate between a more idealistic understanding (logic of inclusion and equality) and a more market-oriented understanding (market logic) of diversity. Our findings show that the founding phase has no significant effect on the likelihood of a university focusing on a market-oriented understanding of diversity - however, we observe an imprinting effect with respect to the adoption of a diversity statement in general and an equity-oriented statement. Moreover, our findings show that there is a socialistic heritage for universities in Central and Eastern European (CEE) countries that is at work and still influences universities’ understandings of diversity today.
https://doi.org/10.1007/s11024-023-09507-x
Towards augmented and mixed reality on future mobile networks. - In: Multimedia tools and applications, ISSN 1573-7721, Bd. 83 (2024), 3, S. 9067-9102
Augmented and Mixed Reality (AR/MR) technologies enhance the human perception of the world by combining virtual and real environments. With the increase of mobile devices and the advent of 5G, this technology has the potential to become part of people’s life. This article aims to evaluate the impact of 5G and beyond mobile networks in the future of AR/MR. To attend to this objective, we surveyed four digital libraries to identify articles and reviews concerning AR/MR use based on mobile networks. The results describe the state-of-the-art of mobile AR/MR applications and the benefits and challenges of the technology. Finally, after the review, we propose a roadmap concerning AR/MR hardware and software development to run applications supported by future mobile networks.
https://doi.org/10.1007/s11042-023-15301-4
Eye and face tracking in VR: avatar embodiment and enfacement with realistic and cartoon avatars. - In: MUM 2023, (2023), S. 270-278
Previous studies have explored the perception of various types of embodied avatars in immersive environments. However, the impact of eye and face tracking with personalized avatars is yet to be explored. In this paper, we investigate the impact of eye and face tracking on embodiment, enfacement, and the uncanny valley with four types of avatars using a VR-based mirroring task. We conducted a study (N=12) and created self-avatars with two rendering styles: a cartoon avatar (created in an avatar generator using a picture of the user’s face) and a photorealistic scanned avatar (created using a 3D scanner), each with and without eye and face tracking and respective adaptation of the mirror image. Our results indicate that adding eye and face tracking can be beneficial for certain enfacement scales (belonged), and we confirm that compared to a cartoon avatar, a scanned realistic avatar results in higher body ownership and increased enfacement (own face, belonging, mirror) - regardless of eye and face tracking. We critically discuss our experiences and outline the limitations of the applied hardware and software with respect to the provided level of control and the applicability for complex tasks such as displaying emotions. We synthesize these findings into a discussion about potential improvements for facial animation in VR and highlight the need for a better level of control, the integration of additional sensing and processing technologies, and an objective metric for comparing facial animation systems.
https://doi.org/10.1145/3626705.3627793
Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts. - In: Journal of cheminformatics, ISSN 1758-2946, Bd. 15 (2023), 122, S. 1-12
This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.
https://doi.org/10.1186/s13321-023-00792-y
Computational modelling and mechanistic insight into light-driven CO dissociation of square-planar rhodium(I) complexes. - In: ChemPhotoChem, ISSN 2367-0932, Bd. n/a (2023), n/a, e202300219, S. 1-28
The activation step of Vaska-type Rh(I) complexes, such as the photocleavage of the Rh‑CO bond, plays an important role in the subsequent C-H activation. To elucidate the details of the photochemistry of Vaska-type Rh(I) complexes, such as trans-Rh(PMe3)2(CO)(Cl), we here present a computationally derived picture as obtained at the density functional level of theory (DFT) in combination with multireference wavefunction-based methods. We have identified that the photocleavage of CO proceeds via the metal-centered excited state, which is populated through intersystem crossing (ISC) from the dipole-allowed excited state S1. Moreover, the present study unraveled the reasons for the low C-H activation efficiency when using Rh featuring the bidentate ligand 1,2-bis(dimethylphosphino)ethane (dmpe), namely due to its unfavorable photochemical properties, i.e., the small driving force for light-induced CO loss and the fast deactivation of 3MC state back to the singlet ground state. In this study, we provide theoretical insight into mechanistic details underlying the light-induced CO dissociation process, for Rh complexes featuring PMe3 and dmpe ligands.
https://doi.org/10.1002/cptc.202300219
Influence of the processing on the properties of continuous fiber reinforced thermoplastic sheets prepared by extrusion. - In: AIP conference proceedings, ISSN 1551-7616, Bd. 2884 (2023), 1, 050005, S. 050005-1-050005-14
Continuous fiber reinforced thermoplastics (CFRT) are composite materials consisting of continuous fibers and a thermoplastic matrix and offer outstanding mechanical properties, low densities, short cycle times and recyclability. CFRT can be classified into unidirectional tapes and sheets utilizing various semi-finished textiles as reinforcement. CFRT sheets are of interest for area measured products or multiaxial loads. Various discontinuous and semi-continuous methods to prepare CFRP sheets are described in the literature. All these methods either feature high cycle times or high investment costs and require double melting of the polymer, e.g., first to produce a polymer film and second to produce the CFRT sheet. An energy efficient alternative to produce CFRT sheets is extrusion, which allows to spare one melting step. A twin-screw extruder melts the polymer, which is then conveyed by a melt pump to the film extrusion dies and applied to both sides of the semi-finished textile, which is wetted and consolidated using a calendar. Due to the high melt viscosity and the line load at the calendar the major challenge is to achieve full void-free impregnation of the semi-finished textile. The mechanical properties of a CFRT sheet are determined by fiber and void volume content. Hence, the influence of the processing conditions on the fiber and void volume content as well as the mechanical properties were examined applying a parametric study of the die temperature, the haul-off speed, and the gap between the calendar rolls. The properties of the extruded CFRT sheets were compared to compression molded sheets. The fiber volume content was directly adjusted by the haul-off speed and the extruder throughput. An increasing die temperature lowers the melt viscosity and results in an increased fiber volume content. Scanning electron microscopy shows complete macro impregnation between the fiber bundles but not completely wetted individual filaments within fiber bundles.
https://doi.org/10.1063/5.0168183
Interactive data cleaning for real-time streaming applications. - In: HILDA '23, (2023), 13, insges. 3 S.
The importance of data cleaning systems has continuously grown in recent years. Especially for real-time streaming applications, it is crucial, to identify and possibly remove anomalies in the data on the fly before further processing. The main challenge however lies in the construction of an appropriate data cleaning pipeline, which is complicated by the dynamic nature of streaming applications. To simplify this process and help data scientists to explore and understand the incoming data, we propose an interactive data cleaning system for streaming applications. In this paper, we list requirements for such a system and present our implementation to overcome the stated issues. Our demonstration shows, how a data cleaning pipeline can be interactively created, executed, and monitored at runtime. We also present several different tools, such as the automated advisor and the adaptive visualizer, that engage the user in the data cleaning process and help them understand the behavior of the pipeline.
https://doi.org/10.1145/3597465.3605229
Spatial prediction of the turbulent unsteady von Kármán vortex street using echo state networks. - In: Physics of fluids, ISSN 1089-7666, Bd. 35 (2023), 11, 115141, S. 115141-1-115141-15
The spatial prediction of the turbulent flow of the unsteady von Kármán vortex street behind a cylinder at Re = 1000 is studied. For this, an echo state network (ESN) with 6000 neurons was trained on the raw, low-spatial resolution data from particle image velocimetry. During prediction, the ESN is provided one half of the spatial domain of the fluid flow. The task is to infer the missing other half. Four different decompositions termed forward, backward, forward-backward, and vertical were examined to show whether there exists a favorable region of the flow for which the ESN performs best. Also, it was checked whether the flow direction has an influence on the network's performance. In order to measure the quality of the predictions, we choose the vertical velocity prediction of direction (VVPD). Furthermore, the ESN's two main hyperparameters, leaking rate (LR) and spectral radius (SR), were optimized according to the VVPD values of the corresponding network output. Moreover, each hyperparameter combination was run for 24 random reservoir realizations. Our results show that VVPD values are highest for LR ≈ 0.6, and quite independent of SR values for all four prediction approaches. Furthermore, maximum VVPD values of ≈ 0.83 were achieved for backward, forward-backward, and vertical predictions while for the forward case VVPDmax = 0.74 was achieved. We found that the predicted vertical velocity fields predominantly align with their respective ground truth. The best overall accordance was found for backward and forward-backward scenarios. In summary, we conclude that the stable quality of the reconstructed fields over a long period of time, along with the simplicity of the machine learning algorithm (ESN), which relied on coarse experimental data only, demonstrates the viability of spatial prediction as a suitable method for machine learning application in turbulence.
https://doi.org/10.1063/5.0172722
An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action. - In: Frontiers in human neuroscience, ISSN 1662-5161, Bd. 17 (2023), 1305331, S. 1-19
A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions - including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings - were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.
https://doi.org/10.3389/fnhum.2023.1305331