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On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
The research project "Herbar Digital" was started in 2007 with the aim to digitize 3.5 million dried plants on paper sheets belonging to the Botanic Museum Berlin in Germany. Frequently the collector of the plant is unknown, so a procedure had to be developed in order to determine the writer of the handwriting on the sheet. In the present work the static character was transformed into a dynamic form. This was done with the model of an inert ball which was rolled along the written character. During this off-line writer recognition, different mathematical procedures were used such as the reproduction of the write line of individual characters by Legendre polynomials. When only one character was used, a recognition rate of about 40% was obtained. By combining multiple characters, the recognition rate rose considerably and reached 98.7% with 13 characters and 93 writers (chosen randomly from the international IAM-database [3]). A global statistical approach using the whole handwritten text resulted in a similar recognition rate. By combining local and global methods, a recognition rate of 99.5% was achieved.
The methods developed in the research project "Herbar Digital" are to help plant taxonomists to master the great amount of material of about 3.5 million dried plants on paper sheets belonging to the Botanic Museum Berlin in Germany. Frequently the collector of the plant is unknown. So a procedure had to be developed in order to determine the writer of the handwriting on the sheet. In the present work the static character is transformed into a dynamic form. This is done with the model of an inert ball which is rolled through the written character. During this off-line writer recognition, different mathematical procedures are used such as the reproduction of the write line of individual characters by Legendre polynomials. When only one character is used, a recognition rate of about 40% is obtained. By combining multiple characters, the recognition rate rises considerably and reaches 98.7% with 13 characters and 93 writers (chosen randomly from the international IAM-database [3]). Another approach tries to identify the writer by handwritten words. The word is cut out and transformed into a 6-dimensional time series and compared e.g. by means of DTW-methods. A global statistical approach using the whole handwritten sentences results in a similar recognition rate of more than 98%. By combining the methods, a recognition rate of 99.5% is achieved.
Wikidata and Wikibase as complementary research data management services for cultural heritage data
(2022)
The NFDI (German National Research Data Infrastructure) consortia are associations of various institutions within a specific research field, which work together to develop common data infrastructures, guidelines, best practices and tools that conform to the principles of FAIR data. Within the NFDI, a common question is: What is the potential of Wikidata to be used as an application for science and research? In this paper, we address this question by tracing current research usecases and applications for Wikidata, its relation to standalone Wikibase instances, and how the two can function as complementary services to meet a range of research needs. This paper builds on lessons learned through the development of open data projects and software services within the Open Science Lab at TIB, Hannover, in the context of NFDI4Culture – the consortium including participants across the broad spectrum of the digital libraries, archives, and museums field, and the digital humanities.
Background
The business of clinical research has changed in the past two decades, shifting from industrialised Western countries to so-called emerging markets such as Eastern Europe, Latin America and Africa. An appraisal of the trends could identify associated factors that may have implications for the local populations and their endemic diseases.
Objectives
To identify potential reasons why emerging countries have become attractive places for international sponsors to conduct their clinical trials.
Methods
Using ClinicalTrials.gov, the Pan African Clinical Trials Registry, the National Health Research Database and the Nigeria Clinical Trials Registry, trend data on clinical research development were determined for two emerging African markets, Nigeria and South Africa (SA), from 2010 to 2018. Also, health data on the two countries from the fact sheets of health statistics of the World Health Organization were compared, as well as regulatory and ethical conditions. Available data were analysed using descriptive statistics and trend analysis.
Results
The impact of globalisation is evident from the upward trend in clinical trials in SA before 2010, and the clear downward trend thereafter. One reason for this change could be the alignment of SA’s regulatory and ethical frameworks with the Western world. In contrast,
the upward trend is only just beginning in Nigeria, with the introduction of ethical/regulatory frameworks, and supportive institutions making the business of clinical research more attractive on an international level. Although the number of international and local sponsors increased in Nigeria from 2010 to 2018, only the latter increased in SA, with the former decreasing over the same period. Overall, there is a mismatch between country-specific diseases and the drugs being tested, to the extent that leprosy, which is endemic in Nigeria, and tuberculosis in SA were not in the list of top 10 study areas in either country.
Conclusions
The globalisation trend is evident in the clinical trials business, but cannot be generalised to all emerging countries. Timing and intensity vary from country to country relative to factors that advance the existing profit-orientated business models of the sponsors. Furthermore, various diseases have been localised, which entails a diversely increasing need for research.
Objectives:
The aim was to identify theoretically expected as well as actually reported benefits from drug development and the importance of individual patient benefits compared to the collective benefits to society in general.
Background:
Ethical guidelines require that clinical research involving humans offer the potential for benefit. A number of characteristics can be applied to define research benefit. Often benefit is categorized as being either direct or indirect. Indirect benefits can involve collective benefits for society rather than any benefits to the trial patient or subject. The purpose of this review was to examine which potential individual and societal benefits were mentioned as being expected in publications from government experts and which were mentioned in publications describing completed drug development trial results.
Methods:
Literature on research benefit was first identified by searching the PubMed database using several combinations of the key words benefit and clinical research. The search was limited to articles published in English. A Google search with the same combinations of key words but without any language limitation was then performed. Additionally, the reference lists of promising articles were screened for further thematically related articles. Finally, a narrative review was performed of relevant English- and German-language articles published between 1996 and 2016 to identify which of several potential benefits were either theoretically expected or which were mentioned in publications on clinical drug development trial results.
Results:
The principal benefits from drug development discussed included 2 main types of benefit, namely individual benefits for the patients and collective benefits for society. Twenty-one of an overall total of 26 articles discussing theoretically expected benefits focused on individual patient benefits, whereas 17 out of 26 articles mentioned collective benefits to society. In these publications, the most commonly mentioned theoretically expected individual patient benefit was the chance to receive up-to-date care (38.1%). A general increase in knowledge about health care, treatments, or drugs (70.6%) was the most commonly mentioned theoretically expected benefit for society. In contrast, all 13 publications reporting actual benefits of clinical drug development trials focused on personal benefits and only 1 of these publications also mentioned a societal benefit. The most commonly mentioned individual benefit was an increased quality of life (53.9%), whereas the only mentioned collective benefit to society was a general gain of knowledge (100.0%).
Conclusions:
Both theoretically expected and actually reported benefits in the majority of the included publications emphasized the importance of individual patient benefits from drug development rather than the collective benefits to society in general. The authors of these publications emphasized the right of each individual patient or subject to look for and expect some personal benefit from participating in a clinical trial rather than considering societal benefit as a top priority. From an ethical point of view, the benefits each individual patient receives from his or her participation in a clinical trial might also be seen as a societal benefit, especially when the drug or device tested, if approved for marketing, would eventually be made available for other similar patients from the country in which the clinical trial was conducted.
Objectives
Quality of care largely depends on successful teamwork, which in turn needs effective communication between health professionals. To communicate successfully in a team, health professionals need to strive for the same goals. However, it has been left largely unaddressed which goals professionals consider to be important. In this study, we aim to identify these goals and analyse whether differences between (1) personal and organisational goals, (2) different professions and (3) hierarchical levels exist in neonatal intensive care units (NICUs).
Design
Goals were identified based on a literature review and a workshop with health professionals and tested in a pilot study. Subsequently, in the main study, a cross-sectional employee survey was undertaken.
Setting and participants
1489 nurses and 537 physicians from 66 German NICUs completed the
questionnaire regarding personal and organisational goal importance between May and July 2013. Answers were given based on a 7-point Likert scale varying between none and exceptionally high importance.
Results
Results show that the goals can be subdivided into three main goal dimensions: patients, parents and staff. Furthermore, our results reveal significant differences between different professions and different hierarchical level: physicians rated patient goals with a
mean (95% CI) importance of 6.37 (3.32 to 6.43), which is significantly higher than nurses with a mean (95% CI) importance of 6.15 (6.12 to 6.19) (p<0.01). Otherwise, nurses classified parental goals as more important (p<0.01). Furthermore, professionals in leading positions rate patient goals significantly higher than professionals that are not in leading positions (6.36 (3.28 to 6.44) vs 6.19 (6.15 to 6.22), p<0.01).
Conclusions
Different employee goals need to be considered in decision-making
processes to enhance employee motivation and the effectiveness of teamwork.
The NOA project collects and stores images from open access publications and makes them findable and reusable. During the project a focus group workshop was held to determine whether the development is addressing researchers’ needs. This took place before the second half of the project so that the results could be considered for further development since addressing users’ needs is a big part of the project. The focus was to find out what content and functionality they expect from image repositories.
In a first step, participants were asked to fill out a survey about their images use. Secondly, they tested different use cases on the live system. The first finding is that users have a need for finding scholarly images but it is not a routine task and they often do not know any image repositories. This is another reason for repositories to become more open and reach users by integrating with other content providers. The second finding is that users paid attention to image licenses but struggled to find and interpret them while also being unsure how to cite images. In general, there is a high demand for reusing scholarly images but the existing infrastructure has room to improve.
Wearable sensors in healthcare and sensor-enhanced health information systems: all our tomorrows?
(2012)
Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuro-psychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health
care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs.
Background: Depletion of ovarian hormone in postmenopausal women has been associated with changes in the locomotor apparatus that may compromise walking function including muscle atrophy/weakness, weight gain, and bone demineralization. Therefore, handgrip strength (HGS), bone mineral density (BMD) and body composition [percentage body fat mass (%BFM), fat mass (FM), Fat-free mass (FFM) and body mass index (BMI)], may significantly vary and predict WB in postmenopausal women. Consequently, the study sought to 1. Explore body composition, BMD and muscle strength differences between premenopausal and postmenopausal women and 2. Explore how these variables [I.e., body composition, BMD and muscle strength] relate to WB in postmenopausal women.
Method: Fifty-one pre-menopausal (35.74 + 1.52) and 50 postmenopausal (53.32 + 2.28) women were selected by convenience sampling and studied. Six explanatory variables (HGS, BMD, %BFM, FFM, BMI and FM) were explored to predict WB in postmenopausal women: Data collected were analyzed using multiple linear regression, ANCOVA, independent t-test and Pearson correlation coefficient at p < 0.05.
Result: Postmenopausal women had higher BMI(t = + 1.72; p = 0.04), %BFM(t = + 2.77; p = .003), FM(t = + 1.77; p = 0.04) and lower HGS(t = − 3.05; p = 0.001),compared to the premenopausal women. The predicted main effect of age on HGS was not significant, F(1, 197) = 0.03, p = 0.06, likewise the interaction between age and %BFM, F(1, 197) = 0.02, p = 0.89; unlike the predicted main effect of %BFM, F(1, 197) = 10.34, p = .002, on HGS. HGS was the highest predictor of WB (t = 2.203; β=0.3046) in postmenopausal women and combined with T-score right big toe (Tscorert) to produce R2 = 0.11;F (2, 47)=4.11;p = 0.02 as the best fit for the predictive model. The variance (R2) change was significant from HGS model (R2 = 0.09;p = 0.03) to HGS + Tscorert model (R2 = 0.11;p = 0.02). The regression model equation was therefore given as: WB =5.4805 + 0.1578(HGS) + (− 1.3532) Tscorert.
Conclusion: There are differences in body composition suggesting re-compartmentalization of the body, which may adversely impact the (HGS) muscle strength in postmenopausal women. Muscle strength and BMD areassociated with WB, although, only contribute to a marginal amount of the variance for WB. Therefore, other factors in addition to musculoskeletal health are necessary to mitigate fall risk in postmenopausal women.
For anomaly-based intrusion detection in computer networks, data cubes can be used for building a model of the normal behavior of each cell. During inference an anomaly score is calculated based on the deviation of cell metrics from the corresponding normality model. A visualization approach is shown that combines different types of diagrams and charts with linked user interaction for filtering of data.
Visual effects and elements in video games and interactive virtual environments can be applied to transfer (or delegate) non-visual perceptions (e.g. proprioception, presence, pain) to players and users, thus increasing perceptual diversity via the visual modality. Such elements or efects are referred to as visual delegates (VDs). Current fndings on the experiences that VDs can elicit relate to specifc VDs, not to VDs in general. Deductive and comprehensive VD evaluation frameworks are lacking. We analyzed VDs in video games to generalize VDs in terms of their visual properties. We conducted a systematic paper analysis to explore player and user experiences observed in association with specifc VDs in user studies. We conducted semi-structured interviews with expert players to determine their preferences and the impact of VD properties. The resulting VD framework (VD-frame) contributes to a more strategic approach to identifying the impact of VDs on player and user experiences.
This study investigates the influence of traumatic events on the mental health of North Korean refugee women by examining the prevalence and severity of posttraumatic stress disorder (PTSD), depression, and anxiety in comparison with their male counterparts (women = 496; men = 131). Our results suggest that women are at greater risk of developing mental health problems than men. In particular, symptoms of PTSD and anxiety were higher among women who experienced forced repatriation to North Korea, which is operationalized as a constellation of gendered traumatic incidents such as sexual abuse, rape, witnessing infanticides, and forced abortion. The policy implications of our results and suggestions for future studies are discussed.
The use of vibrational sum-frequency spectroscopy (VSFS) to study transferred graphene, produced by chemical vapour deposition, is presented. The VSF spectrum shows a clear CeH stretching mode at ~2924 cm⁻¹, which is attributed to residue of the polymer used for the transfer. This makes VSFS a powerful tool to identify adsorbates and contaminants affecting the properties of graphene.
We report velocity-dependent internal energy distributions of nitric oxide molecules, NO, scattered off graphene supported on gold to further explore the dynamics of the collision process between NO radicals and graphene. These experiments were performed by directing a molecular beam of NO onto graphene in a surface-velocity map imaging setup, which allowed us to record internal energy distributions of the NO radicals as a function of their velocity. We do not observe bond formation but (1) major contributions from direct inelastic scattering and (2) a smaller trapping–desorption component where some physisorbed NO molecules have residence times on the order of microseconds. This is in agreement with our classical molecular dynamics simulations which also observe a small proportion of two- and multi-bounce collisions events but likewise a small proportion of NO radicals trapped at the surface for the entire length of the molecular dynamics simulations (a few picoseconds). Despite a collision energy of 0.31 eV, which would be sufficient to populate NO(v = 1), we do not detect vibrationally excited nitric oxide.
Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific exts outperform word embeddings learned from much larger general corpora.
Using openEHR Archetypes for Automated Extraction of Numerical Information from Clinical Narratives
(2019)
Up to 80% of medical information is documented by unstructured data such as clinical reports written in natural language. Such data is called unstructured because the information it contains cannot be retrieved automatically as straightforward as from structured data. However, we assume that the use of this flexible kind of documentation will remain a substantial part of a patient’s medical record, so that clinical information systems have to deal appropriately with this type of information description. On the other hand, there are efforts to achieve semantic interoperability between clinical application systems through information modelling concepts like HL7 FHIR or openEHR. Considering this, we propose an approach to transform unstructured documented information into openEHR archetypes. Furthermore, we aim to support the field of clinical text mining by recognizing and publishing the connections between openEHR archetypes and heterogeneous phrasings. We have evaluated our method by extracting the values to three openEHR archetypes from unstructured documents in English and German language.
In huge warehouses or stockrooms, it is often very difficult to find a certain item, because it has been misplaced and is therefore not at its assumed position. This position paper presents an approach on how to coordinate mobile RFID agents using a blackboard architecture based on Complex Event Processing.