A SOCIO-TECHNICAL PERSPECTIVE ON REPRODUCIBILITY IN RESEARCH DATA MANAGEMENT

The Open Science paradigm has brought the dissemination of experimental artifacts on the agenda of funding agencies, research institutions, and academic publishers. Managing research data is a crucial part of guaranteeing the reusability and reproducibility of published results. In this research, we suggest a conceptualization of reproducibility based on threats, risks, and vulnerabilities identified in current research data management (RDM) practices. By doing so, we can describe a range of threats to reproducibility and pinpoint areas where current RDM practices and the scholarly communication infrastructure insufficiently address these threats. Further, we elaborate on a socio-technical approach to reproducibility in RDM by collecting evidence from researchers and scientific publications. We show that the STS approach complements current IS research on RDM by offering a holistic view of reproducibility challenges in RDM

ISFAM: THE INFORMATION SECURITY FOCUS AREA MATURITY MODEL

Information security is mainly a topic that is considered to be information technology related. However, to successfully implement information security, an organization´s information security program should reflect the business strategy. Nowadays information security is in many companies enforced by the information technology department, based on what they think should be in place to protect their business from inside and outside threats and risks. Additionally, information security covers many different subjects. This makes it especially hard for small and medium sized organizations to determine how they should design their information security program. \ \ Therefore, we present the Information Security Focus Area Maturity Model (ISFAM). By identifying dependencies between various aspects of information security and representing them coherently in the ISFAM, the model is capable of determining the current information security maturity level. Involving the ISFAM model in the design process of an organization´s information security program enables organizations to set up high level guidelines based on their current status. These guidelines can be used to incrementally and structurally improve information security maturity within the organization. We have successfully evaluated the ISFAM assessment model through a single case study at a medium sized telecommunications organization

UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation

This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relationship that holds between a noun pair contained in the sentence. For sub-task 1 -- binary classification -- we propose an effective way to enhance the robustness and the generalizability of language models for better classification on this downstream task. We design a two-stage fine-tuning procedure on the ELECTRA language model using data augmentation techniques. Rigorous experiments are carried out using multi-task learning and data-enriched fine-tuning. Experimental results demonstrate that our proposed model, UU-Tax, is indeed able to generalize well for our downstream task. For sub-task 2 -- regression -- we propose a simple classifier that trains on features obtained from Universal Sentence Encoder (USE). In addition to describing the submitted systems, we discuss other experiments that employ pre-trained language models and data augmentation techniques. For both sub-tasks, we perform error analysis to further understand the behaviour of the proposed models. We achieved a global F1_Binary score of 91.25% in sub-task 1 and a rho score of 0.221 in sub-task 2

Крест как символ парадигмального образа эпохи христианской цивилизации

Убедительнее всего проследить становление невидимой структуры символа и обретение подлинного органического единства видимого знака символа и его невидимой структуры на примере знака креста как символа парадигмального образа эпохи христианской цивилизации. Это и сделано в данном материале.Переконливіше за всього простежити становлення невидимої структури символу та обретення дійсної органичної єдності видимого знаку символа та його невидимої структури на прикладі знаку хреста як символа парадигмального образу епохі християнської цивілізації. Це й зроблено у даному матеріалі.It is the most convincing to trace formation of invisible structure of symbol and finding of real organic unity of visible sign of symbol and its invisible structure on the example of the sign of cross as the symbol of paradigmal image of epoch of christian civilization. It is done in given material

WHAT CONCERNS USERS OF MEDICAL APPS? EXPLORING NON-FUNCTIONAL REQUIREMENTS OF MEDICAL MOBILE APPLICATIONS

The increased use of internet through smartphones and tablets enables the development of new consumer-focused mobile applications (apps) in health care. Concerns including these apps´ safety, usability, privacy, and dependability have been raised. In this paper the authors present the results of a grounded theory-approach to finding what non-functional requirements of medical apps potential users view as most important. A document study and interviews with stakeholders yielded nine non-functional requirements for medical apps: accessibility, certifiability, portability, privacy, safety, security, stability, trustability, and usability. Six of these were evaluated with two groups (differing by age) of potential users through a vignette study. This revealed differences between the age groups regarding the importance each attributed to apps´ usability and certifiability. Furthermore, and contrary to consensus in literature, privacy was considered one of the least important attributes for medical apps by both groups. Trustability, security, and, for the younger group, certifiability, were considered the most important non-functional requirements for medical apps. The implications of these results for developing medical mobile applications are briefly visited

A Comparative Study of Fuzzy Topic Models and LDA in terms of Interpretability

In many domains that employ machine learning models, both high performing and interpretable models are needed. A typical machine learning task is text classification, where models are hardly interpretable. Topic models, used as topic embeddings, carry the potential to better understand the decisions made by text classification algorithms. With this goal in mind, we propose two new fuzzy topic models; FLSA-W and FLSA-V. Both models are derived from the topic model Fuzzy Latent Semantic Analysis (FLSA). After training each model ten times, we use the mean coherence score to compare the different models with the benchmark models Latent Dirichlet Allocation (LDA) and FLSA. Our proposed models generally lead to higher coherence scores and lower standard deviations than the benchmark models. These proposed models are specifically useful as topic embeddings in text classification, since the coherence scores do not drop for a high number of topics, as opposed to the decay that occurs with LDA and FLSA

К юбилею ученого, учителя, человека (75 лет доктору экономических наук, профессору Валентину Николаевичу Гончарову)

An important strategic decision within the food industry is to achieve the optimal product portfolio allowing an organization to support its customers in the best possible way. Numerous models exist to categorize products based on their relative metrics like revenue, volume, margin or constructed scores. In order to make a more profound decision whether to keep or to remove a product from the portfolio a closer look into product interdependencies is desirable. Hence, by exploring existing DM-techniques through literature and evaluating those DM-techniques that seem suited in a PPM-context by applying each to a dataset, we aim to identify those techniques that complement a Product Portfolio Management process in the food industry. Three DM-techniques were selected: Dependency Modeling, Change and Deviation Detection, and Classification. Of these three techniques, two were found to be of complementary value in a PPM-context, Dependency modeling and Classification, respectively. Change and deviation detection was found to be of no complementary value in a PPM-context due to it forecasting future data points based on historical data, which results in future data points never exceeding the maximum historical data points. However, change and deviation detection could be of complementary value in another context. Finally, we propose an a lgorithm to visualize the data-driven product classifications in a standard po rtfolio matrix which p ortfolio managers can intuitively understand

STRIPA: A Rule-Based Decision Support System for Medication Reviews in Primary Care

The chronic use of multiple medicinal drugs is growing, partly because individual patients’ drugs have not been adequately prescribed by primary care physicians. In order to reduce these polypharmacy problems, the Systematic Tool to Reduce Inappropriate Prescribing (STRIP) has been created. To facilitate physicians’ use of the STRIP method, the STRIP Assistant (STRIPA) has been developed. STRIPA is a stand-alone web-based decision support system that advices physicians during the pharmacotherapeutic analysis of patients’ health records. In this paper the application’s architecture and rule engine, and the design decisions relating to the user interface and semantic interoperability, are described. An experimental validation of the prototype by general practitioners and pharmacists showed that users perform significantly better when optimizing medication with STRIPA than without. This leads the authors to believe that one process-oriented decision support system, built around a context-aware rule engine, operated through an intuitive user interface, is able to contribute to improving drug prescription practices

UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation

This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relationship that holds between a noun pair contained in the sentence. For sub-task 1—binary classification—we propose an effective way to enhance the robustness and the generalizability of language models for better classification on this downstream task. We design a two-stage fine-tuning procedure on the ELECTRA language model using data augmentation techniques. Rigorous experiments are carried out using multi-task learning and data-enriched fine-tuning. Experimental results demonstrate that our proposed model, UU-Tax, is indeed able to generalize well for our downstream task. For sub-task 2 —regression—we propose a simple classifier that trains on features obtained from Universal Sentence Encoder (USE). In addition to describing the submitted systems, we discuss other experiments that employ pre-trained language models and data augmentation techniques. For both sub-tasks, we perform error analysis to further understand the behaviour of the proposed models. We achieved a global F1$Binary$ score of 91.25% in sub-task 1 and a rho score of 0.221 in sub-task 2