Reviewerly/exHarmony huggingface dataset:
Uncertainty estimation-based adversarial attacks: a viable approach for graph neural networks

Abstract Uncertainty estimation has received momentous consideration in applied machine learning to capture model uncertainty. For instance, the Monte-Carlo dropout method (MC-dropout), an approximated Bayesian approach, has gained intensive attention in producing model uncertainty due to its simplicity and efficiency. However, MC-dropout has revealed shortcomings in capturing erroneous predictions lying in the overlapping classes. Such predictions underlie noisy data points that can neither be reduced by more training data nor detected by model uncertainty. On the other hand, Monte-Carlo based on adversarial attacks (MC-AA), an outstanding method, performs perturbations on the inputs using the adversarial attack idea to capture model uncertainty. This method admittedly mitigates the shortcomings of the previous methods by capturing wrong labels in overlapping regions. Motivated by this method that was only validated with neural networks, we sought to apply MC-AA on various graph neural network models to obtain uncertainties using two public real-world graph datasets known as Elliptic and GitHub. First, we perform binary node classifications, then we apply MC-AA and other recent uncertainty estimation methods to capture the uncertainty of the models. Uncertainty evaluation metrics are computed to evaluate and compare the performance of the uncertainty of the model. We highlight the efficacy of MC-AA in capturing uncertainties in graph neural networks wherein MC-AA outperforms other given methods.
Embedding channel pruning within the CNN architecture design using a bi-level evolutionary approach

Remarkable advancements have been achieved in machine learning and computer vision through the utilization of deep neural networks. Among the most advantageous of these networks is the convolutional neural network (CNN). It has been used in pattern recognition, medical diagnosis, and signal processing, among other things. Actually, for these networks, the challenge of choosing hyperparameters is of utmost importance. The reason behind this is that as the number of layers rises, the search space grows exponentially. In addition, all known classical and evolutionary pruning algorithms require a trained or built architecture as input. During the design phase, none of them consider the process of pruning. In order to assess the effectiveness and efficiency of any architecture created, pruning of channels must be carried out before transmitting the dataset and computing classification errors. For instance, following pruning, an architecture of medium quality in terms of classification may transform into an architecture that is both highly light and accurate, and vice versa. There exist countless potential scenarios that could occur, which prompted us to develop a bi-level optimization approach for the entire process. The upper level involves generating the architecture while the lower level optimizes channel pruning. Evolutionary algorithms (EAs) have proven effective in bi-level optimization, leading us to adopt the co-evolutionary migration-based algorithm as a search engine for our bi-level architectural optimization problem in this research. Our proposed method, CNN-D-P (bi-level CNN design and pruning), was tested on the widely used image classification benchmark datasets, CIFAR-10, CIFAR-100 and ImageNet. Our suggested technique is validated by means of a set of comparison tests with regard to relevant state-of-the-art architectures.
TextDescriptives: A Python package for calculating a large variety of metrics from text

Natural language processing (NLP) tasks often require a thorough understanding and description of the corpus.Document-level metrics can be used to identify low-quality data, assess outliers, or understand differences between groups.Further, text metrics have long been used in fields such as the digital humanities where e.g.metrics of text complexity are commonly used to analyse, understand and compare text corpora.However, extracting complex metrics can be an error-prone process and is rarely rigorously tested in research implementations.This can lead to subtle differences between implementations and reduces the reproducibility of scientific results.TextDescriptives offers a simple and modular approach to extracting both simple and complex metrics from text.It achieves this by building on the spaCy framework (Honnibal et al., 2020).This means that TextDescriptives can easily be integrated into existing workflows while leveraging the efficiency and robustness of the spaCy library.The package has already been used for analysing the linguistic stability of clinical texts (Hansen et al., 2022), creating features for predicting neuropsychiatric conditions (Hansen et al., 2023), and analysing linguistic goals of primary school students (Tannert, 2023).
Using machine learning for healthcare treatment planning

We present a methodology for using machine learning for planning treatments. As a case study, we apply the proposed methodology to Breast Cancer. Most of the application of Machine Learning to breast cancer has been on diagnosis and early detection. By contrast, our paper focuses on applying Machine Learning to suggest treatment plans for patients with different disease severity. While the need for surgery and even its type is often obvious to a patient, the need for chemotherapy and radiation therapy is not as obvious to the patient. With this in mind, the following treatment plans were considered in this study: chemotherapy, radiation, chemotherapy with radiation, and none of these options (only surgery). We use real data from more than 10,000 patients over 6 years that includes detailed cancer information, treatment plans, and survival statistics. Using this data set, we construct Machine Learning classifiers to suggest treatment plans. Our emphasis in this effort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient.
Designing for Care

This article introduces Designing for Care (D4C), a distinctive approach to project management and technological design informed by Care Ethics. We propose to conceptualize "care" as both the foundational value of D4C and as its guiding mid-level principle. As a value, care provides moral grounding. As a principle, it equips D4C with moral guidance to enact a caring process. The latter is made of a set of concrete, and often recursive, caring practices. One of the key assumption of D4C is a relational ontology of individual and group identities, which fosters the actualization of caring practices as essentially relational and (often) reciprocal. Moreover, D4C adopts the "ecological turn" in CE and stresses the ecological situatedness and impact of concrete projects, envisioning an extension of caring from intra-species to inter-species relations. We argue that care and caring can influence directly some of the phases and practices within the management of (energy) projects and the design of sociotechnical (energy) artefacts and systems. When issues related to "value change" emerge as problematic (e.g., values trade-offs, conflicts), the mid-level guiding principle of care helps evaluate and prioritize different values at stake within specific projects. Although there may be several actors and stakeholders involved in project management and technological design, here we will focus on the professionals in charge of imagining, designing, and carrying out these processes (i.e., project managers, designers, engineers). We suggest that adopting D4C would improve their ability to capture and assess stakeholders' values, critically reflect on and evaluate their own values, and judge which values prioritize. Although D4C may be adaptable to different fields and design contexts, we recommend its use especially within small and medium-scale (energy) projects. To show the benefits of adopting it, we envisage the application of D4C within the project management and the technological design of a community battery. The adoption of D4C can have multiple positive effects: transforming the mentality and practice of managing a project and designing technologies; enhancing caring relationships between managers, designers, and users as well as among users; achieving better communication, more inclusive participation, and more just decision-making. This is an initial attempt to articulate the structure and the procedural character of D4C. The application of D4C in a concrete project is needed to assess its actual impact, benefits, and limitations.
Digital Interaction with Physical Museum Artifacts

In the digital information world, visualizing information in public spaces has been implemented in various formats and for application contexts such as advertisement, useful information provision, and provision of critical information in the cases of accidents, natural disasters, etc. Among the different types of information displays, in this research work, the focus is given to the ones that extend the experience of people visiting cultural heritage institutions. To this end, the design and implementation of an interactive display case that aims to overcome the “non-touch policy” of museums are presented. This novel display allows visitors to get engaged with artifacts and information through touch-based interaction with the ambition to extend the target audience and impact of museum content. The conducted study demonstrates that the interactive display case is an effective solution for providing relevant information to visitors, enhancing their engagement with exhibits, and improving their overall experience. The proposed solution is user-friendly, engaging, and informative, making it ideal for museums and other public exhibit spaces.
The Role of Employee Empowerment in Supporting Accounting Information Systems Outcomes: A Mediated Model

The current research study was carried out to explore the mediating influence of management awareness on the relationship between employee empowerment and accounting information systems (AIS) outcomes. A quantitative approach was adopted through the distribution of an online questionnaire to (97) financial managers and accounting managers within banks in Jordan. SPSS was used in order to analyze the primary data, and it was seen that management awareness mediates the relationship between employee empowerment and AIS outcomes, which is attributed to the fact that management needs to be aware of organizational goals and the financial information needed to achieve these goals. This includes an understanding of the latest accounting technology that is available to assist in this process. Additionally, management needs to be aware of the costs associated with the implementation of new systems, and any potential risks that could arise. By being actively involved in the implementation and decision-making process, management can ensure that AIS is efficient and produces the desired results. Finally, management should ensure that appropriate user training is available for all users of the information system. This ensures that the system can be utilized in the most efficient manner possible and produces the desired financial results. This study recommended the need to utilize integrated cloud-based systems to increase the availability of information and reduce the need for manual data entry. Further recommendations were presented in the study.
Prototype‐based method for incremental radar emitter identification

Abstract With the widespread use of radars, different types of radar emitters are being used in the real electromagnetic environment. Radar emitter identification (REI) is an important technique in spectrum management. Methods based on deep learning have been successful in REI. However, they are difficult to be updated with signals from newly available categories. Additionally, two issues that must be considered in a real REI task. First, signals from new unknown radar emitters may appear in the identification stage. Hence the model used in REI must have an open‐set recognition capability. Second, obtaining numerous labelled samples in time are difficult in the incremental stage of the model. Thus, it is important to keep the model performance stable under conditions of small samples. To solve these problems, a one‐dimensional neural network is designed combined with ArcFace loss in the initial training stage to reserve more embedding space for future new classes, thereby facilitating the update of the model and open‐set recognition. An unbiased cosine similarity classifier was adopted, and the historical categories were memorised by their prototypes. When a new category is added, the prototype was calculated and the classifier weights were updated. The proposed method can identify unknown classes and add them to the model when given new labels, thereby resulting in an increase of the model's identifiable types. Extensive experiments were performed. The results show that the proposed model is highly efficient and requires small sample sizes, thereby making it suitable for REI.
An Optimized Implementation of Activation Instruction Based on RISC-V

Activation is an important component of the neural network, and the standard instructions of RISC-V are difficult to use to effectively handle the activation of the array. In this paper, we propose an optimized implementation of activation instruction based on RISC-V. Based on the opensource RISC-V processor Hummingbird E203, we designed a special instruction for the implementation of activation functions. A single instruction is chosen to implement the entire activation operation, including data loading, data arithmetic and data write-back. At the hardware level, we designed a method of alternate reading and writing that only needs a small hardware storage unit to meet the requirements of the activation operation for long arrays without affecting the activation efficiency. In addition, we added the length of the array as a new parameter to instruct our designed hardware to adapt to any length of arrays. Finally, the scheduling method of some instructions in the activation process was optimized in accordance with the law of instructions, which improves the execution efficiency of instructions. Considering an activation process with an array length of 15, our design demonstrates a 4.89-fold increase in speed compared to RISC-V standard instructions while consuming only 7.78% of the energy.
Switching network for mixing experts with application to traffic sign recognition

Abstract The correct and robust recognition of traffic signs is indispensable to self-driving vehicles and driver-assistant systems. In this work, we propose and evaluate two network architectures for multi-expert decision systems that we test on a challenging Traffic Sign Recognition Benchmark dataset. The decision systems implement individual experts in the form of deep convolutional neural networks (CNNs). A gating network CNN acts as final decision unit and learns which individual expert CNNs are likely to contribute to an overall meaningful classification of a traffic sign. The gating network then selects the outputs of those individual expert CNNs to be fused to form the final decision. In this work we study the advantages and challenges of the proposed multi-expert architectures that in comparison to other network architectures allow for parallel training of individual experts with reduced datasets. Under the challenging conditions introduced by the benchmark dataset, the demonstrated multi-expert decision systems achieve a recognition performance that is superior to those of humans: with an accuracy of 99.10%, when training experts with the complete dataset and 98.94%, when individual experts are only trained with 36% of the training samples. Overall, our approach ranked fourth on the list of the applied approaches proposed for the German traffic sign Recognition Benchmark (GTSRB) dataset.
Challenges and Strategies in Conducting Population Health Research during the COVID-19 Pandemic: Experience from a Nationwide Mixed-Methods Study in Bangladesh

Introduction: Globally, the coronavirus (COVID-19) pandemic poses fundamental challenges in everyday life. Various controlling measures, including nationwide lockdowns, movement restrictions, travel bans, social distancing, and improved hygiene practices, have been widely introduced to curtail transmission of the disease. Notably, these measures have affected the execution of population health research that typically involves face-to-face data collection. This paper details a subjective reflective account of the challenges and mitigating strategies in conducting a nationwide study during the COVID-19 pandemic in 2021. Challenges and strategies: The research team faced a wide range of challenges in conducting this study. The major categories of challenges were defined as follows: (i) challenges relating to the COVID-19 pandemic, such as insufficient access to field sites; (ii) challenges related to contextual factors, such as cultural and gender sensitivity and extreme weather events; and (iii) challenges related to data quality and validity. The key mitigating strategies to overcoming these challenges included engaging a local-level field supervisor, hiring data collectors from respective study sites, incorporating team members' reviews of literature and experts' views to develop research instruments, modifying original research instruments, organizing regular meetings and debriefing, adjusting field operation plans, building gender-sensitive teams, understanding local norms and adopting culturally appropriate dress codes, and conducting interviews in local languages. Conclusions: This paper concludes that despite several COVID-19-related challenges coupled with contextual factors, data were successfully collected through timely and successful adaptations of several mitigating strategies. The strategies adopted in this study may be useful for overcoming unforeseeable challenges in planning and conducting future population-based health research in similar circumstances elsewhere.
Information Extraction Network Based on Multi-Granularity Attention and Multi-Scale Self-Learning

Transforming the task of information extraction into a machine reading comprehension (MRC) framework has shown promising results. The MRC model takes the context and query as the inputs to the encoder, and the decoder extracts one or more text spans as answers (entities and relationships) from the text. Existing approaches typically use multi-layer encoders, such as Transformers, to generate hidden features of the source sequence. However, increasing the number of encoder layers can lead to the granularity of the representation becoming coarser and the hidden features of different words becoming more similar, potentially leading to the model's misjudgment. To address this issue, a new method called the multi-granularity attention multi-scale self-learning network (MAML-NET) is proposed, which enhances the model's understanding ability by utilizing different granularity representations of the source sequence. Additionally, MAML-NET can independently learn task-related information from both global and local dimensions based on the learned multi-granularity features through the proposed multi-scale self-learning attention mechanism. The experimental results on two information extraction tasks, named entity recognition and entity relationship extraction, demonstrated that the method was superior to the method based on machine reading comprehension and achieved the best performance on the five benchmark tests.
Effects of coupling with a quantum oscillator on time-evolution of uncertainties of a quantum particle and entanglement entropy

The time evolution of a quantum particle’s product of uncertainties in position and momentum is calculated when it is coupled with an external source. We have used a simple toy model where the particle is subject to a harmonic potential and coupled with an equivalent harmonic oscillator via a linear term. It is found that the long-time-averaged product is an increasing function of the coupling strength. It diverges when one of the eigenmodes of the coupled system goes soft, with the singular term twice of that for the stationary state. Generally, there is a jump of finite size for this quantity when a small coupling is turned on, compared to the uncoupled case. Similar behaviors have also been found for the von Neumann entanglement entropy, which is calculated exactly using a covariance matrix formalism. We find that the mode-interference plays an important role in the main features of this work.
Approaches and tools for user-driven provenance and data quality information in spatial data infrastructures

Geospatial data are fundamental in most global-change and sustainability-related domains. However, readily accessible information on data quality and provenance is often missing or hardly accessible for users due to technical or perceptual barriers, for example, due to unstructured metadata information or missing references. Within an interdisciplinary process encompassing perspectives of data users, data producers, and software developers, we identified major needs to facilitate effective fitness-for-use assessments by data users and developed approaches to address these. We provided a stylized analysis of large-scale land use data to showcase selected approaches. To support data users, interoperable quality and provenance information need to be meaningfully represented. Data producers need efficient workflows and tools supporting them in creating high-quality, structured and detailed quality and provenance information. Our newly developed approaches to increase the availability of structured metadata synthesize new and existing tools to extract metadata or to generate provenance data during processing. Within our approaches to improve interoperability and accessibility we present novel tools to support (i) the creation of curated and linked registers of data quality indicators and thematic terms, and (ii) linked visualization of data quality and provenance information. Following our approaches increases transparency, facilitates fitness-for-use assessments, and ultimately improves research quality.
Robust Quantum Arithmetic Operations with Intermediate Qutrits in the NISQ-era

Numerous scientific developments in this NISQ-era (Noisy Intermediate Scale Quantum) have raised the importance for quantum algorithms relative to their conventional counterparts due to its asymptotic advantage. For resource estimates in several quantum algorithms, arithmetic operations are crucial. With resources reported as a number of Toffoli gates or T gates with/without ancilla, several efficient implementations of arithmetic operations, such as addition/subtraction, multiplication/division, square root, etc., have been accomplished in binary quantum systems. More recently, it has been shown that intermediate qutrits may be employed in the ancilla-free frontier zone, enabling us to function effectively there. In order to achieve efficient implementation of all the above-mentioned quantum arithmetic operations with regard to gate count and circuit-depth without T gate and ancilla, we have included an intermediate qutrit method in this paper. Future research aiming at reducing costs while taking into account arithmetic operations for computing tasks might be guided by our resource estimations using intermediate qutrits. Therefore, the enhancements are examined in relation to the fundamental arithmetic circuits. The intermediate qutrit approach necessitates access to higher energy levels, making the design susceptible to errors. We nevertheless demonstrate that the percentage decrease in the probability of error is significant due to the fact that we achieve circuit efficiency by reducing circuit-depth in comparison to qubit-only works.