Reviewerly/exHarmony huggingface dataset:
Calculation of the moscovium ground‐state energy by quantum algorithms

Abstract We investigate the possibility to calculate the ground‐state energy of the atomic systems on a quantum computer. For this purpose we evaluate the lowest binding energy of the moscovium atom with the use of the iterative phase estimation and variational quantum eigensolver (VQE). The calculations by the VQE are performed with a disentangled unitary coupled cluster ansatz and with various types of hardware‐efficient ansatze. The optimization is performed with the use of the Adam and quantum natural gradients procedures. The scalability of the ansatze and optimizers is tested by increasing the size of the basis set and the number of active electrons. The number of gates required for the iterative phase estimation and VQE is also estimated.
Investigating the Landscape of Ontologies for Catalysis Research Data Management

This work provides a survey of ontologies for catalysis research to improve the findability, accessibility, interoperability, and reusability (FAIRness) of research data. Applying tools that are commonly used by lab scientists, ontologies relevant to catalysis research are classified in a simple, well formatted spreadsheet template (Excel). This enables a scientist and domain expert without programming skills to evaluate a certain ontology. The entries of this template are then processed and visualized through automated creation of markdown files on GitHub using Python scripts. Furthermore, ontology mapping by searching for similar pairs of classes across different ontologies is performed, using the outcome of the ontology classification. This work contributes to the development of ontologies for catalysis research, facilitating better data integration and knowledge sharing while reusing existing semantic artefacts.
Data Publication for Personalised Health Data

Health data collected in clinical trials and epidemiological as well as public health studies cannot be freely published, but are valuable datasets whose subsequent use is of high importance for health research. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) aims to promote the publication of such health data without compromising privacy. Based on existing international standards, NFDI4Health has established a generic information model for the description and preservation of high-level metadata describing health-related studies, covering both clinical and epidemiological studies. As an infrastructure for publishing such preservation metadata as well as more detailed representation information of study data (e.g. questionaries and data dictionaries), NFDI4Health has developed the German Central Health Study Hub. Content is either harvested from existing distributed sources or entered directly via a user interface. This metadata makes health studies more discoverable, and researchers can use the published metadata to evaluate the content of data collections, learn about access conditions and how and where to request data access. The goal of NFDI4Health is to establish interoperable and internationally accepted standards and processes for the publication of health data sets to make health data FAIR.
Research Data Management Curriculum of the Research Data Services at the University Library Duisburg-Essen

Reproducible and transparent science requires good research data management (RDM). However, researchers are often not familiar with how to appropriately handle research data, publication options, legal aspects, and the multitude of available software tools. To support scientists at the University of Duisburg-Essen in gaining RDM knowledge and competencies, the Research Data Services developed a structured teaching program – the RDM Curriculum – for researchers from different disciplines and career levels.&#x0D; The RDM Curriculum is separated into a basic and an advanced module. In the basic module, participants can select from three full-day courses that address the basics of RDM with different disciplinary focus. The advanced module consists of various 2 hour online hands-on courses on diverse RDM topics and tools. As incentive to attend multiple courses, participants can earn the RDM Badge of the University Library Duisburg-Essen. The curriculum program is complemented by specialized RDM courses and self-learning materials.&#x0D; With the RDM Curriculum, we created an easily accessible and individually selectable RDM course offer that enables researchers to gain discipline-specific and individually relevant RDM knowledge. The curriculum structure can gradually be expanded to further target groups (e.g. students) and allows for continuously adding new contents in the advanced module. The high request for the RDM Badge after the first iteration of the RDM Curriculum in the winter semester 2022/2023 confirmed the positive effect of incentives. Consequently, we are planning to establish a Data Champion Award as appreciation of RDM achievements and to showcase best-practice examples at the University of Duisburg-Essen.
Optical phase retrieving of a projected object by employing a differentiation of a single pattern of two-beam interference

In this work, we present a new approach to retrieve the optical phase map of an object which is projected by a single differentiated two-beam interference pattern. This approach is based on the differentiation of the intensity equation of the two-beam interference with respect to the carrier's phase angle. Therefore, two interference patterns which are shifted by a very small phase angle can be obtained. Then, these two patterns are projected on the object. By exploiting the definition of the mathematical differentiation, the optical phase object's variations are retrieved from the recorded intensity distributions of both projected patterns. According to this method, the extracted optical phase angles are raised as an inverse "sin" function. This means that the unwrapping process of this function limits the recovered phase angles between - π/2 and π/2. So, the unwrapping process of these unusual wrapped phase angles is explained. The proposed method is applied on (a) two objects which are simulated by combinations of multiple Gaussian functions and (b) a 3D real object. It is found that the inclination of the projected interference pattern on the object redistributes the intensity distribution due to the Lamber's "cos" aw of illumination. This effect is considered in the retrieving process of the object's phase map. The limitations of the presented method are discussed and the obtained results are found promising.
Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.
Error mitigation for quantum approximate optimization

Quantum error mitigation involves reducing errors in hybrid quantum-classical algorithms, which is particularly relevant for near-term noisy quantum devices. The authors introduce an error mitigation technique based on redundant parity encoding of logical variables. They demonstrate the use of this technique by applying it to the quantum approximate optimization algorithm.
Estimation of optimal control for two-level and three-level quantum systems with bounded amplitude

Abstract A systematic scheme is proposed to numerically estimate the quantum speed limit and temporal shape of optimal control in two-level and three-level quantum systems with bounded amplitude. For the two-level system, two quantum state transitions are studied as illustration. Comparisons between numerical and analytical results are made, and deviations are significantly small. For the three-level system, two critical time points are determined with high accuracy, and optimal controls are obtained for different durations. The shape of optimized control field is simple and does not switch frequently, thus are easy to implement in experiment. In addition, we compare our method with the chopped random basis (CRAB), and the performance of our method is much better than that of CRAB. Our scheme is of importance in estimating the quantum speed limit and optimal control for cases in which analytical solution is absent.
Morphological Box for AI Solutions: Evaluation and Refinement with a Taxonomy Development Method

Investigations into the organisational uptake of artificial intelligence (AI) solutions confirm that there is a growing interest in enterprises and public authorities to use AI. In this context, the lack of understanding of AI concepts in organisations is a significant challenge. As a contribution to addressing this issue, we previously developed and evaluated a morphological box for AI solutions. To further refine this morphological box, the paper follows a well-established scientific method for this purpose: This paper presents the application of a taxonomy development method to our morphological box. We use this method to determine a meta-characteristic, identify the target audience, project the use of the morphological box, and define both subjective and objective ending conditions. We describe several iterations of the development and evaluation loops and present our final results. Our analysis demonstrates the effectiveness of the taxonomy development method in refining and enhancing the morphological box for AI solutions. We further present the application of the morphological box for classifying AI projects with four initial case studies, discuss the results as well as further development directions and potentials of the box.
An encrypted medical blockchain data search method with access control mechanism

The use of medical blockchain enables the safe sharing of electronic medical data (EMD). With the continuous growth of EMD, how to efficiently find EMD on the medical blockchain is a very challenging problem. In this paper, we propose an encrypted medical blockchain data search method with an access control mechanism. Firstly, we elaborate on the importance of medical blockchain data search, access control, and encryption to enhance our research motivation. Secondly, we detail the implementation process and security analysis of our method. Finally, the experimental results on Ethereum, nonrelational database MongoDB, and real public medical data show that compared with previous work, the proposed method can improve search efficiency while preserving privacy.
Knowledge-Concept Diagnosis from fMRIs by Using a Space-Time Embedding Graph Convolutional Network

Diagnosing the contents of learning in brain activities is a long-standing research task in cognitive sciences. The current studies on cognitive diagnosis (CD) in education determine the status of knowledge concept (KC) based on the observed responses to test items. However, the learning process of KC in the brain is left with no touch. This paper proposes to solve the problem of knowledge-concept diagnosis (KCD) from fMRIs by identifying the concepts a student focuses on in learning activities. Using the graph convolutional network (GCN), we introduce the STEGCN approach composed of a spatial GCN for brain-graph structure, a temporal GCN for brain-activity sequence, and a fully connected network for KCD. To evaluate STEGCN, we acquired an fMRI dataset that was collected on five concepts when students were learning a computer course. The experiment results demonstrate that our proposed method yields better performance than traditional models, showing the effectiveness of STEGCN in concept classification. This study contributes to a new fMRI-based route for knowledge-concept diagnosis.
MAR-Net: Motion-Assisted Reconstruction Network for Unsupervised Video Summarization

Video summarization targets to extract the most important segments from a video by spatiotemporal analysis. Previous methods primarily learn content within videos based on appearance information, with a rare discussion on the effective utilization of motion information, which is equally essential to video understanding. In this letter, we expound upon a Motion-Assisted Reconstruction Network (MAR-Net), which synergistically models appearance and motion information within videos for unsupervised video summarization without any manual annotations. MAR-Net notably comprises a Bidirectional Modality Encoder (BiME) and a Video Context Navigator (VCN). By integrating uni-modal and cross-modal feature aggregation into a unified module, BiME allows for exploring sophisticated dependency relationships among features through a bidirectional attention mechanism. VCN can promote the semantic consistency between the cross-modal contexts and the input video by a consistency loss term, alleviating the noisy impact within the motion stream. Empirical results conducted on benchmark datasets demonstrate that MAR-Net outperforms other state-of-the-art methods.
DINE: Dynamic Information Network Embedding for Social Recommendation

The social recommendation aims to integrate social network information to improve the accuracy of traditional recommender systems. Learning embeddings of nodes in networks is one of the core problems of many applications such as recommendation, link prediction, and node classification. Early studies cast the social recommendation task as a vertex ranking problem. Although these methods are effective to some extent, they require assuming social networks and user-item interaction networks as static graphs, whereas real-world information networks evolve over time. In addition, the existing works have primarily focused on modeling users in social networks in general and overlooked the special properties of items. To address these issues, we propose a new method named DINE, short for Dynamic Information Network Embedding, to learn the vertex representations for dynamic networks in social recommendation task. We model both users and items simultaneously and integrate the representations in dynamic and static information networks. In addition, the multi-head self-attention mechanism is employed to model the evolution patterns of dynamic information networks from multiple perspectives. We conduct extensive experiments on Ciao and Epinions datasets. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our DINE method.
Transitioning a project-based course between onsite and online. An experience report

We present an investigation regarding the challenges faced by student teams across four consecutive iterations of a team-focused, project-based course in software engineering. The studied period includes the switch to fully online activities in the spring of 2020, and covers the return to face to face teaching two years later. We cover the feedback provided by over 1500 students, collected in free-text form on the basis of a survey. A qualitative research method was utilized to discern and examine the challenges and perceived benefits of a course that was conducted entirely online. We show that technical challenges remain a constant in project-based courses, with time management being the most affected by the move to online. Students reported that the effective use of collaborative tools eased team organization and communication while online. We conclude by providing a number of action points regarding the integration of online activities in face-to-face course unfolding related to project management, communication tools, the importance of teamwork, and of active mentor participation.
Speaker Anonymization Using Orthogonal Householder Neural Network

Speaker anonymization aims to conceal a speaker's identity while preserving content information in speech. Current mainstream neural-network speaker anonymization systems disentangle speech into prosody-related, content, and speaker representations. The speaker representation is then anonymized by a selection-based speaker anonymizer that uses a mean vector over a set of randomly selected speaker vectors from an external pool of English speakers. However, the resulting anonymized vectors are subject to severe privacy leakage against powerful attackers, reduction in speaker diversity, and language mismatch problems for unseen-language speaker anonymization. To generate diverse, language-neutral speaker vectors, this paper proposes an anonymizer based on an orthogonal Householder neural network (OHNN). Specifically, the OHNN acts like a rotation to transform the original speaker vectors into anonymized speaker vectors, which are constrained to follow the distribution over the original speaker vector space. A basic classification loss is introduced to ensure that anonymized speaker vectors from different speakers have unique speaker identities. To further protect speaker identities, an improved classification loss and similarity loss are used to push original-anonymized sample pairs away from each other. Experiments on VoicePrivacy Challenge datasets in English and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AISHELL-3</i> dataset in Mandarin demonstrate the proposed anonymizer's effectiveness.
RDM Compas: Building Competencies for the Professional Curation of Research Data

“RDM Compas: Research Data Management Competence Base”, consisting of an education and training centre with modular online trainings on the one hand, and a comprehensive knowledge base covering all topics of curation-specific RDM on the other hand. In addition, a certification option is envisioned for curation-specific RDM competencies. In the context of our presentation, due to the advanced state of work we want to focus on the knowledge base and present its structure and elements. The structural basis is a slightly simplified version of the Data Curation Lifecycle Model [2] offered by the UK Digital Curation Centre [3]. This lifecycle describes both the basic activities and the sequential process steps of data curation and therefore provides a helpful schema for teaching the necessary RDM competencies.
Text-based Question Difficulty Prediction: A Systematic Review of Automatic Approaches

Abstract Designing and constructing pedagogical tests that contain items (i.e. questions) which measure various types of skills for different levels of students equitably is a challenging task. Teachers and item writers alike need to ensure that the quality of assessment materials is consistent, if student evaluations are to be objective and effective. Assessment quality and validity are therefore heavily reliant on the quality of the items included in the test. Moreover, the notion of difficulty is an essential factor that can determine the overall quality of the items and the resulting tests.Thus, item difficulty prediction is extremely important in any pedagogical learning environment. Although difficulty is traditionally estimated either by experts or through pre-testing, such methods are criticised for being costly, time-consuming, subjective and difficult to scale, and consequently, the use of automatic approaches as proxies for these traditional methods is gaining more and more traction. In this paper, we provide a comprehensive and systematic review of methods for the priori prediction of question difficulty. The aims of this review are to: 1) provide an overview of the research community regarding the publication landscape; 2) explore the use of automatic, text-based prediction models; 3) summarise influential difficulty features; and 4) examine the performance of the prediction models. Supervised machine learning prediction models were found to be mostly used to overcome the limitations of traditional item calibration methods. Moreover, linguistic features were found to play a major role in the determination of item difficulty levels, and several syntactic and semantic features were explored by researchers in this area to explain the difficulty of pedagogical assessments. Based on these findings, a number of challenges to the item difficulty prediction community are posed, including the need for a publicly available repository of standardised data-sets and further investigation into alternative feature elicitation and prediction models.
Method to Improve the Cryptographic Properties of S-Boxes

This study presents a method based on elementary logic and arithmetic operations to enhance the cryptographic properties of Substitution Boxes (S-Boxes). S-Boxes are a crucial component of cryptosystems, as they apply the confusion principle to information before it is encrypted, making them vital for ensuring the security of sensitive information transmitted through insecure channels. The proposed method employs bitwise XOR, Modular Addition, and Circular Shift operations, which are applied to selected S-Boxes, resulting in numerous S-Box variants that have no fixed points or reverse fixed points. We found that some of these variants can increase nonlinearity when using modular addition or circular shift operations and are therefore more suitable for use in cryptosystems. Our study contributes to the understanding of how S-Boxes can be enhanced by elementary logic and arithmetic operations. We recommend using the proposed method with the bitwise XOR operation when the S-Box has high nonlinearity (112) but requires removing fixed points and reverse fixed points. Otherwise, first use modular addition or circular shift operations to increase nonlinearity.
MGDIN: Detail Injection Network for HSI and MSI Fusion Based on Multiscale and Global Contextual Features

Hyperspectral remote sensing images (HSI) are characterized as rich spectral information with low spatial resolution, and a cost-effective way for the spatial information supplement is the fusion to multispectral remote sensing images (MSI). This study proposed a detail injection network for HSI and MSI fusion based on multiscale and global contextual features (MGDIN), which extracts features of different scales using residual multi-scale convolution, and captures the contextual information and long-range dependencies via global contextual block. MGDIN improves the spatial and spectral qualities of fused images by minimizing a new loss function that considers content, spectral and edge losses. Experiments on five publicly available datasets (including Botswana, Pavia Centre, Pavia University, Washington DC Mall and Houston) show that MGDIN outperforms the popular algorithms in terms of fusion quality and learning ability. The new loss function is also better than the popular loss functions in the experiments on the Botswana data set.