Reviewerly/exHarmony huggingface dataset:
The artificial intelligence pizza

In this chapter, you discover how artificial intelligence (AI) technology can offer pharmaceutical care providers smart support systems, given that it has access to complete, adequate, and holistic health data sets. AI can support trend analysis and decision-making that augment pharmaceutical care expertise. This is what we may call “apothecary intelligence.” This chapter provides examples of big platforms as well as startup concepts related to AI healthcare. This chapter also dives into transparency, detection of data bias, competency building, and ethical considerations. With AI-driven pharmacy-as-a-service platforms, pharmaceutical care providers can spend more time working on what they do best: using human judgment, empathy, and consideration to provide good patient care.
Improving ethical usage of corporate data in higher education: Enhanced Enterprise Data Ethics Framework

Purpose The purpose of this study is to address the generalised lack of guidance on ethical treatment of corporate (e.g. non-research) data in higher education institutions, by focusing on the case of the University of Queensland (Brisbane, Australia). No actionable framework is currently available in the country to govern the ethical usage of corporate data. As such, this research takes a stakeholder-centred approach to data ethics; the lived experience of the stakeholders involved coupled with a theory-based ethical framework allowed the authors build to build a framework to guide ethical data practice. Design/methodology/approach Adopting a revised canonical action research approach focused on intervention on the context, the authors conducted a review of the literature on ethical usage of data in higher education institutions; administered one survey to university students ( n = 168); and facilitated three workshops with professional staff (two) and students (one). Findings Collected data highlighted how, among other themes, the role and ethical importance of transparency was the dominant claim among all stakeholder groups. Findings helped the authors develop an Enhanced Enterprise Data Ethics Framework (EEDEF) emphasising transparency and stakeholder-centricity. Practical implications Legislation is the driver to regulate the use of corporate data in higher education; however, this can be problematic because legislation is retrospective, lacks normativity and offers scarce directions for cases that do not exactly follow within the legislative mandate. In light of these regulatory limitations, the authors’ EEDEF offers operators guidance on how to ethically manage corporate data in the higher education environment. Originality/value This study fills gaps in praxis and theory; that is the lack of literature and guiding ethical frameworks to inform data practice in higher education. This research fosters a more ethical data management by virtue of genuine and authentic engagement with stakeholders and emphasises the importance of strategic decision-making and maturity of data culture in the higher education sector.
Model-based Testing for a Family of Mobile Applications

Testing is a fundamental verification activity to produce high-quality software. However, testing is a costly and complex activity. The success of software testing depends on the quality of test cases but finding a good set of test cases is laborious. To make matters worse, when dealing with a family of systems (e.g., variants of a mobile applications), test cases must assure that a diversity of configurations in potentially many variants work as expected. This is the case of hello again GmbH, a company that develops mobile applications for customer loyalty (e.g., discounts, free products, rewards, or insider perks). The company targets several business domains, and currently supports about 700 application variants. Testing such applications including all their variability is a cumbersome task. Even simple test cases designed for one variant most likely cannot be reused for other variants. To support developers at hello again GmbH, we present a solution to employ a model-based testing approach to their family of mobile apps. Model-based testing focuses on automatizing the design and generation of test cases. We present results of applying model-based testing on 27 applications from hello again GmbH and report the challenges and lessons learned for designing a variable test model. Our expected contribution is to support companies and practitioners looking for solutions to test families of software products.
Data Management Plan Implementation, Assessments, and Evaluations: Implications and Recommendations

Data management plans (DMPs) have become nearly a worldwide requirement for research funding. To meet these new funding agency expectations, information professionals across domains and the world have worked to create resources and services to successfully implement and sometimes assess DMPs. This essay presents a series of case studies from different institutions across the globe to highlight current practices and share recommendations for future work. A summary of various projects related to DMP implementation, assessment, and evaluation in different contexts provides a useful overview of current practices. The essay concludes with recommendations for practical oversight and scoring to improve DMPs&rsquo; utility in enabling the sharing of data.
Exploring the Potential of Metaverse Apps for Real-World Applications: A Case Study with CALEND_AR

This paper presents CALEND_AR, a hybrid calendar application that uses augmented reality technologies to blend physical and digital calendars. The study investigates user preferences and motivations for using physical or digital calendars and explores the need for a hybrid solution that incorporates both. It comprehensively describes the application’s implementation process, including technological background, design considerations, and a user study. Evaluation results show that the hybrid approach combining the benefits of paper-based and digital calendars was appreciated by participants, despite limitations such as inconsistent accuracy of text recognition and mixed user reception of the augmented reality feature. The contributions of the work include the development of a unique and innovative hybrid approach to calendar management and demonstrating the potential of metaverse apps to address real-world problems and enhance people’s lives. Future research could explore ways to improve text recognition accuracy and make the augmented reality feature more user-friendly. Overall, CALEND_AR represents an important step forward in designing augmented and mixed reality applications.
Taming the Diversity of Computational Notebooks

In many applications of Computational Science and especially Data Science, notebooks are the cornerstone of knowledge and experiment sharing. Their diversity is multiple (problem addressed, input data, algorithm used, overall quality) and is not made explicit at all. As they are heavily reused through a clone-and-own approach, the tailoring process from an existing notebook to a specific problem is cumbersome, error-prone, and particularly uncertain. In this paper, we propose a tooled approach that captures the different dimensions of variability in computational notebooks. It allows one to seek an existing notebook that suits her requirements, or to generate most parts of a new one.
Family-based model checking of fMultiLTL properties

We introduce a new logic for expressing multi-properties of system families (Software Product Lines - SPLs). While the standard LTL logic refers only to a single trace at a time, fMultiLTL logic proposed here refers to multiple traces originating from different sets of variants of the SPL. This is achieved by allowing so-called featured quantification over traces, ∀ψ and ∃ψ, where the feature expression ψ describes a set of variants (sub-family) the quantified trace comes from. A specialized family-based model checking algorithm for verifying some fragments of fMultiLTL is given. A prototype family-based model checker, called Dądalux, has been implemented. We illustrate the practicality of this approach on several interesting SPL models.
Your chef: the virtual pharmaceutical care assistant

Conversational AI has undergone huge evolution in the last decades, as chatbots were augmented with improved AI, text-to-speech, and visual empathic features. Nowadays, Virtual Personal (health care) Assistants (VPAs) are quickly finding their way into health care to support healthcare systems and triage, diagnostic, and disease management applications. In the pharmaceutical arena, these VPAs can support blended pharmaceutical care as an augmentation of human care, for example, by providing drug information, adherence support, or consulting for adequate, rational drug use. To ensure continuous updating and consistency in the pharmaceutical content of Digital Pharmacists, coding and classification of medication information and resources is required, as much as other conditions to succeed, like security, privacy, and ethical prerequisites. The time that digital humans can replace pharmacists from a knowledge and communication point of view is far away. Nevertheless, it will never replace the most important element in health care: human attention. With the use of digital humans for noncomplex consulting that can be done digitally, more time is freed up for the ultimate human care in a healthcare system under pressure.
Blockchain and Intelligent Computing Framework for Sustainable Agriculture

Any country's economy must prioritise the sustainable development of agriculture and related services. Understanding the effect of intelligent computing on sustainable agriculture requires examining the different challenges this study has brought up. About 600 research papers on intelligent computing for sustainable agriculture were gathered for the qualitative study. Additionally, 66 papers from the research papers that took a proper screening for relevance were selected. These papers have been divided into three categories: blockchain applications for sustainable agriculture, blockchain applications for IoT in agriculture, and the impact of machine learning and blockchain on agriculture. It was noted in all of the studies that there is growing interest in using technology for sustainable agriculture, and it was also noted that the majority of the study has only recently been started. Therefore, the application of modern technologies for security, trust, and confidentiality among stockholders may make sustainable agriculture viable.
Using Polymorphic Glyphs to Support the Visual Exploration of Hierarchical Spatio-Temporal Data

Studying spatio-temporal data is essential to understand the processes of the real world. However, the design of effective visualizations to explore spatio-temporal data is not a straightforward task due to the inherent multidimensional aspects of the data. In this paper, we explore the usage of polymorphous glyphs (i.e. glyphs that change shapes according to the context) to support the exploration of multiple hierarchical levels and dimensions of the data. We implemented our approach in the form of a web-based visualization interface that we demonstrate through a case study of the ISSA KG, which describes scientific publications in the field of agriculture, thus supporting domain experts on investigating where, when and how different crops are cultivated.
Comparative analysis of experimental data clustering in MATLAB and Python environment

The paper compares normalization and clustering methods for processing experimental data in the MATLAB package and a Python program using the scikit-learn library. Recommendations on further application of Python programs and selection of normalization and clustering methods are provided. The choice of clustering methods for further research is discussed.
ID-Based Cryptography and Attribute-Based Cryptography

Identity and attribute-based cryptosystems have now become the most interesting research area in cryptography. Id-based and attribute-based cryptography both are improved variants of public-key cryptography. The identity-based cryptography is based on the identity of the user, such as email address, IP address, or mobile number. On the other hand, in attribute-based cryptography users encrypt and decrypt the message based on their attributes. Both IBE and ABE are based on mathematical concepts like Integer factorization, Quadratic Residues, Discrete logarithm Problems, Diffie-Hellman problems, and Bilinear pairings. IBE allows users to encrypt and decrypt messages using their identities whereas ABE provides fine-grain access control, it allows organizations and individuals to access data based on specific attributes or properties. IBE and ABE have various applications in cloud computing, digital signatures, and secure data sharing in IoT. Most ABE schemes based on access structure can be categorized as Key-Policy ABE (KP-ABE) and ciphertext-based ABE (CP-ABE).
Econometric modeling of the calculation of gross domestic product with the expense method

The results of the correlation between the factors affecting the gross domestic product were calculated on the basis of the methodology of the national accounting system, a regression equation was identified, and short-term alternative forecasting issues were clarified using a statistical index of GDP growth in 1997-2019 of the Republic of Uzbekistan in this article.
A Unified Detection Approach for Point and Subsequence Anomaly Data From Train Axle Temperature Sensors

The train axle temperature sensor data exhibit two distinct abnormal patterns: point anomalies and subsequence anomalies, which reflect the intricate operational state of the axle and train. Precise detection of multicategory anomalies within the axle temperature data is crucial for ensuring the safe and stable operation of trains. However, the coexistence of multiple anomaly types leads to diminished accuracy in existing anomaly detection methods. Hence, we propose an unsupervised unified detection model, leveraging the power of the prediction model and targeting both point and subsequence anomalies. Our model begins by utilizing long short-term memory (LSTM) networks to predict the axle temperature, thereby generating a residual sequence. To ameliorate the error accumulation effect of LSTM, a weighting mechanism is introduced during the prediction process. Subsequently, the kernel density estimation (KDE) method is adopted to perform piecewise nonparametric fitting on the distribution of the residual sequence, enabling the determination of nonparametric dynamic thresholds for point anomalies. Finally, using the detected point anomalies as boundary points, the residual sequence is partitioned into multiple subsequences of varying lengths. The improved local outlier factor (ILOF) method is applied to compute the anomaly score of each subsequence, enabling the recognition of subsequence anomalies. The evaluation experiments for anomaly detection were conducted on two axle temperature datasets. The F1-scores were all above 0.975, which verified the effectiveness of the model. Compared with the baseline models, the F1-score of the proposed model has increased by more than 5%, which verified the superiority of the proposed model.
Digital Modeling for Everyone: Exploring How Novices Approach Voice-Based 3D Modeling

Manufacturing tools like 3D printers have become accessible to the wider society, making the promise of digital fabrication for everyone seemingly reachable. While the actual manufacturing process is largely automated today, users still require knowledge of complex design applications to produce ready-designed objects and adapt them to their needs or design new objects from scratch. To lower the barrier to the design and customization of personalized 3D models, we explored novice mental models in voice-based 3D modeling by conducting a high-fidelity Wizard of Oz study with 22 participants. We performed a thematic analysis of the collected data to understand how the mental model of novices translates into voice-based 3D modeling. We conclude with design implications for voice assistants. For example, they have to: deal with vague, incomplete and wrong commands; provide a set of straightforward commands to shape simple and composite objects; and offer different strategies to select 3D objects.
A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for Solving Complex Optimization Problems

Hunger Games Search (HGS) is a newly developed metaheuristic algorithm that models the hunger-driven activities and behaviors of animals. It incorporates the concept of hunger to devise an adaptive weight that mimics the impact of hunger on each search step. In this paper, a Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm (MASPE-HGSA) is proposed to alleviate the shortcomings of the original HGS in terms of insufficient optimization and convergence accuracy. In MASPE-HGSA, a Multi-Stage adaptive sequential parameter exploration is proposed to improve the search performance of the algorithm as well as to increase the search accuracy and global search capability, which can commendably achieve the balance of exploration and exploitation. The effectiveness of MASPE-HGSA is verified by comparing with original HGS algorithm and several classical algorithms using 23 benchmark functions, CEC2014 test set and three advanced algorithmic problems from classical engineering. Experimental results show that the performance of MASPE-HGSA is significantly better than other similar algorithms. The proposed algorithm can effectively search for high-quality solutions and prevent premature convergence, with better convergence robustness than the original HGS algorithm. In addition, an analysis of probability-based algorithm mechanisms approaching zero is presented in this paper from a theoretical perspective, while providing a reference and verification method for the design of algorithm mechanisms.
How Many Participants Do You Need for an Open Card Sort? A Case Study of E-commerce Websites

Open card sorting is the most used method for developing user-centered information architectures. One important question for every HCI method is how many users to involve. Existing studies that address this question for open card sorts have involved trained professionals sorting content items of rather specialized domains. In addition, they employ data analysis approaches that might decrease the confidence one can place on the reported findings. This paper investigates the minimum number of participants for open card sorts performed on a general public website domain (e-commerce). In specific, it involves 203 and 210 participants sorting content items of two real-world e-commerce websites. Results from all the participants were compared with those of different-sized and randomly selected samples of the participants. It was found that 15 to 20 participants is a cost-effective way to obtain reliable open card sort data for general public websites.
New Version of Fast Automatic Determination of Cluster Numbers for High Dimensional Big Data

The main issue in clustering algorithms is how to efficiently define number of clusters automatically. Considering both the quality of clustering and efficiency of clustering algorithm during determination of number of clusters can be a trade off that was our main purpose to overcome with. Successfully, In our approach the best number of clusters for a large data set of high dimensional data automatically would be determined with respect to clustering quality and efficiency. We carried out experimental studies on our five previous data sets [43]. and 4 new larger ones by which we found that our procedure has the flexibility of choosing different criteria to determine the optimal K under each of them. The procedure takes the advantages of Bisecting K-Means algorithm, and indicated higher efficiency compared with Ray & Turi method when it comes to find the best number of clusters.
Is There a Limit to the Utility of Analogy? A Case Study in Knowledge Graph Triple Representation

Knowledge graph embedding methods are known to be highly dependent on the locally closed world assumption (LCWA). This assumption has practicality in training neural networks for the link prediction task, but is ill-posed for representing knowledge graph triples as first class objects. In this paper, we explore an alternate sampling paradigm, namely pairwise triple similarity scoring (PTSS), and detail the impact of the sampling parameter on downstream predicate prediction tasks. We specifically seek to find the limit at which more negative samples do not provide a statistically significant lift in performance, giving insights into how to efficiently sample and train such models. Our main finding indicates that there is a point of diminishing return on the number of analogous, pairwise samples selected; this point can be found prior to experimental hyperparameter sweeps. Additionally, our experiments show that there are two classes of models: those that benefit from additional sampling, and those that are less impacted. The root cause for these differences is driven by changes in the distributions of similarity scores, depending on the seed knowledge graph embeddings selected. Our work demonstrates the importance of selecting the correct seed embedding method, largely dependent on the topology of the underlying knowledge graph.
True Variability Shining Through Taxonomy Mining

Software variants of a Software Product Line (SPL) consist of a set of artifacts specified by features. Variability models document the valid relationships between features and their mapping to artifacts. However, research has shown inconsistencies between the variability of variants in features and artifacts, with negative effects on system safety and development effort. To analyze this mismatch in variability, the causal relationships between features, artifacts, and variants must be uncovered, which has only been addressed to a limited extent. In this paper, we propose taxonomy graphs as novel variability models that reflect the composition of variants from artifacts and features, making mismatches in variability explicit. Our evaluation with two SPL case studies demonstrates the usefulness of our variability model and shows that mismatches in variability can vary significantly in detail and severity.
Rethinking Antitrust for the Cloud Era

This article asks how antitrust can foster innovation by examining the development of infrastructure for data processing in the cloud. We contrast Amazon Web Services' centralized model with Google Cloud Platform's more decentralized, participatory ecosystem. We argue that rather than trying to reduce the power imbalance between platforms and independent database companies, antitrust should seek to channel platforms from the centralized model toward the decentralized ecosystem by (1) making partnership more attractive than mergers and (2) enlisting open-source foundations to help manage interoperability in the cloud. This requires breaking down the silos between competition, technology, and industrial policy.