Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Energy Domain Ontologies

The energy domain is part of Critical Infrastructures (CI), in which Smart Grids (SG) play the role of major enabler of concepts such as Smart Cities. The capability of reasoning through ontologies is of paramount importance to allow a better integration of sensors and devices part of the smart energy domain. In this paper, we provide a comprehensive evaluation of semantic reasoning in the energy domain, by evaluating the performance of ten ontologies in terms of different metrics such as load time and run-time performance, discussing how such ontologies can be applicable in case of deployment on devices with constrained resources.
Robustness and Explainability of Image Classification Based on QCNN

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL.
Hospitality management in times of crisis: a corporate social responsibility perspective

Purpose From a hospitality and tourism perspective, the purpose of this study is to analyse the case of Jordan by looking at the implementation of corporate social responsibility (CSR) practices in the hospitality industry and their contribution to the management of the pandemic crisis, with a special focus on large hotels. Design/methodology/approach The method focuses on a qualitative study based on ten in-depth interviews with senior managers of five-star hotels in Jordan, fully used as quarantine facilities during the COVID-19 pandemic. Findings The study reveals how and why the implementation of CSR practices contributes to the crisis management in Jordan, also highlighting the role of the managers and the hotels’ organizational cultures. Originality/value Drawing from the unique case of Jordan, to the best of the authors’ knowledge, this is the first study which analyses the close relationships between crisis and hospitality management from a CSR perspective, and the impact of organizational cultures and ethical strategies on local stakeholders.
Code Generation by Example Using Symbolic Machine Learning

Abstract Code generation is a key technique for model-driven engineering (MDE) approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language. Specialised code generation languages and tools have been defined; however, the task of manually constructing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code generation language. In this paper, we apply novel symbolic machine learning techniques for learning tree-to-tree mappings of software syntax trees, to automate the development of code generators from source–target example pairs. We evaluate the approach on several code generation tasks, and compare the approach to other code generator construction approaches. The results show that the approach can effectively automate the synthesis of code generators from examples, with relatively small manual effort required compared to existing code generation construction approaches. We also identified that it can be adapted to learn software abstraction and translation algorithms. The paper demonstrates that a symbolic machine learning approach can be applied to assist in the development of code generators and other tools manipulating software syntax trees.
Initial Implementation of Data Analytics and Audit Process Management

To answer the call for more evidence on the adoption and effectiveness of Big Data Analytics in auditing, this study investigates auditors’ use of data analytic tools in audit-process management, including audit planning, testing, and conclusions. The analysis, which is performed as a qualitative study, is based on twenty-eight semi-structured interviews with Big 4 and non-Big 4 audit professionals in Thailand to gain insights into their experience implementing audit data analytic tools in the initial stage. Findings suggest that auditors primarily use data analytic tools in audit planning and substantive testing. Nevertheless, auditors do not perceive a need to use these tools to test internal controls and conclude audit opinions. In addition, we find that auditors tend to apply audit data analytic tools for anomaly detection and testing management assertions. Overall, auditors perceive the benefits of audit data analytic tools in improving their audit process management. Findings present practical implications for audit firms and audit professionals, including how to initially implement data analytic tools effectively in auditing and as guidelines for regulators on how to develop auditing standards that govern the use of Big Data and data analytic tools. We note some limitations in this study, such as the generalizability of the results, auditors’ personal biases, and the different tools and techniques used by each audit firm.
Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation

Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient's conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requires diverse information to evaluate a patient from different perspectives to obtain an accurate diagnosis. Recently proposed medical question generation systems have not considered diversity. Thus, we propose a diversity learning-based visual question generation model using a multi-latent space to generate informative question sets from medical images. The proposed method generates various questions by embedding visual and language information in different latent spaces, whose diversity is trained by our newly proposed loss. We have also added control over the categories of generated questions, making the generated questions directional. Furthermore, we use a new metric named similarity to accurately evaluate the proposed model's performance. The experimental results on the Slake and VQA-RAD datasets demonstrate that the proposed method can generate questions with diverse information. Our model works with an answering model for interactive automated clinical diagnosis and generates datasets to replace the process of annotation that incurs huge labor costs.
Edammo’s Extreme AutoML Technology – Benchmarks and Analysis

Whether a model is utilizing classical machine learning (ML) algorithms (e.g., Support Vector Machines, KNN, etc.) or neural architectures (e.g., Deep Learning), one must choose a method for selecting optimal hyperparameters. There are both manual and automated methods for hyperparameter optimization. Automated methods are generally referred to as automated machine learning, or AutoML [1]. Several automated selection algorithms have shown similar or improved performance over state-of-the-art methods [2–6]. This breakthrough has led to the development of cloud-based services like Google AutoML [8], which is based on Deep Learning [9] and is widely considered to be the industry leader in AutoML services. We use a fundamentally different type of neural architecture: Extreme Learning Machines (ELMs) [7–13]. ELMs are generally less computationally expensive than Deep Learning. We benchmark our Extreme AutoML technology versus Google's AutoML using four popular classification datasets from the University of California at Irvine's (UCI) repository [27]. We observe significant advantages for Extreme AutoML in accuracy, Jaccard Indices [28], the variance of Jaccard Indices across classes (i.e. class variance) and training times.
Randomized Model Structure Selection Approach for Extreme Learning Machine Applied to Acid Sulfate Soil Detection

This paper investigates the performance of Extreme Learning Machine in acid sulfate soil classification task. A special focus is made on a robust model structure selection by randomized parameter search. The resulting model structures are compared using two datasets about the same phenomenon but of a very different nature: one with hand-crafted scalar features, and the other with 9 $$\times $$ 9 pixels image patches instead of features. The results highlight the flexibility of the presented model selection approach.
Leveraging CNN and Transfer Learning for Classification of Histopathology Images

This paper aims to build a hybrid convolutional neural architecture by leveraging the power of pre-trained ResNet 50 (trained on ImageNet dataset) through transfer learning. The proposed work has achieved state-of-the-art performance metrics on the BreakHis dataset, containing microscopic histopathological images of benign and malignant breast tumours. The model incorporates global average pooling, dropout and batch normalisation layers on top of the pre-trained ResNet50 backbone. This methodical superimposing of the GAP layer in tandem with Resnet50's knowledge and training is the proposed novelty taking our model the extra mile. As a result, the binary classification problem between benign and malignant tumours is handled gracefully by our proposed architecture despite the target imbalance. We achieve an AUC of 0.946 and an accuracy of 98.7% which is better than the previously stated standard.
Practical teaching method innovation decisions related to labor and reading at agricultural colleges based on entropy-fuzzy AHP combination weights

&lt;abstract&gt; &lt;p&gt;Due to the rapid development of the economy, science, and technology, we have noticed a trend where less and less attention is being paid to the modernization and development of labor and reading concerning agricultural teaching methods. Using the entropy weight method and the analytic hierarchy process, this paper empirically evaluates the practical aspects of interdisciplinary farming at agricultural colleges. In addition, it analyzes problems with current practical teaching methods from the perspectives of curriculum materials, practical education, educational platforms, and teachers. We also put forward corresponding ideas and suggestions for practical teaching methods related to interdisciplinary farming. The results show the following: ⅰ) A student's sense of responsibility to develop agriculture should be cultivated by integrating the cultural elements of agriculture with science curricula. ⅱ) Faculties with knowledge of interdisciplinary practices should be set up to improve students' abilities regarding practical activities. ⅲ) A practical teaching platform should be established to provide conditions for interdisciplinary integration. ⅳ) The previous conclusion requires schools to cultivate high-level teachers to maximize their leading role; only in this way can the process of agricultural education be comprehensively promoted. In this paper, by studying practical farming teaching methods at agricultural colleges and universities, we found that farming culture can be enhanced by educational culture. The cultivation of agricultural talents in the new era can be aided by helping students understand and appreciate agriculture, and by disseminating farming culture, agricultural talent can be cultivated among these students. Furthermore, this study focuses on cultivating excellent talent in terms of technical agriculture and rural management, thus combining theory and practice, which will promote the spirit of labor among students, as well as professional quality. This will ensure the constant innovation of agricultural and nonagricultural professional services for agriculture, rural areas, and farmers.&lt;/p&gt; &lt;/abstract&gt;
A formal GSPN model of a virtual doctor dialogue system

This paper presents a formal generalized stochastic Petri net (GSPN)–based modeling of a virtual doctor (VDr) dialogue system. The interaction model between the VDr dialogue system and the human patient is a modified version of the Wickens model. The purpose of this model is to provide a better understanding of the interaction between the VDr dialogue system and the patient, as well as the integration of other components in the system, such as the patient’s medical history and emotions. Initially, the formal definition of the GSPN model of the VDr dialogue system is presented by incorporating the token color concept, which is a feature of Color GSPNs. Then, the GSPN formal modeling is described. Note that, the GSPN model presented here is at the simulation level and not a product. Furthermore, some examples are provided with the intention of facilitating the understanding of the token color utilization and the different GSPN markings.
Data governance: Digital product passports as a case study

The circular economy (‘CE’) is a political goal to shift the economy towards a more circular and sustainable one. Data sharing shall close information gaps in the predominant economy and, hence, form the backbone of the transition to a CE. As part of the recent legislative proposals from the EC, “digital product passports” (‘DPPs’) are expected to constitute one-stop shops for such data and information delivery. The principal idea of DPPs is to provide product information to interested stakeholders within and outside the product value chain. However, many challenges arise with DPPs, in particular how to reconcile a broad (if no ‘open’) access to data while preserving the (legally protected) interests of certain actors, and the lack of trust concerning data use and quality between the actors. Our hypothesis are (i.) that such challenges are related to the absence of an appropriate (data) governance model for DPPs and (ii.) that data governance as a main concern within the data economy agenda could inform the regulation and operationalisation of DPPs. We analyse the – lack of - (data) governance of DPPs, both in the literature and in the recent legislative proposals from the EC. We find that data governance is significantly overlooked or considered only from a pure technological perspective (techno-solutionism), which is likely to jeopardise DPPs. As both a confirmation and a way forward, we establish connections to data governance as per the data economy agenda. In turn, the analysis of DPPs governance informs the general discussion on data governance.
Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices

Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.
Self-supervised Image-based 3D Model Retrieval

Image-based 3D model retrieval aims at organizing unlabeled 3D models according to the relevance to the labeled 2D images. With easy accessibility of 2D images and wide applications of 3D models, image-based 3D model retrieval attracts more and more attentions. However, it is still a challenging problem due to the modality gap between 2D images and 3D models. In spite of the remarkable progress brought by domain adaptation techniques for this research topic, which usually propose to align the global distribution statistics of two domains, these methods are limited in learning discriminative features for target samples due to the lack of label information in target domain. In this article, besides utilizing the label information of 2D image domain and the adversarial domain alignment, we additionally incorporate self-supervision to address cross-domain 3D model retrieval problem. Specifically, we simultaneously optimize the adversarial adaptation for both domains based on visual features and the contrastive learning for unlabeled 3D model domain to help the feature extractor to learn discriminative feature representations. The contrastive learning is used to map view representations of the identical model nearby while view representations of different models far apart. To guarantee adequate and high-quality negative samples for contrastive learning, we design a memory bank to store and update representative view for each 3D model based on entropy minimization principle. Comprehensive experimental results on the public image-based 3D model retrieval datasets, i.e., MI3DOR and MI3DOR-2, have demonstrated the effectiveness of the proposed method.
Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet , an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.
Cognitive elements of learning and discriminability in anti-phishing training

People adjust decisions based on their experiences; and it is important to know how to shape these experiences effectively to improve their future decisions. We conducted laboratory experiments to study an effective way to shape end-users' experiences to improve their detection of phishing emails. Despite technical and training-based solutions to phishing detection, malicious emails continue to reach people broadly. The end-user's susceptibility towards phishing emails could be due to lack of experience in phishing, lack of motivation or inappropriate knowledge of phishing cues. In this paper, we study the effects of two experimental variables during a training phase: The frequency of phishing emails (low frequency-25%, medium frequency-50%, and high frequency-75%), and the type of feedback provided on the decisions made (outcome or detail feedback). The individual's base susceptibility to phishing emails was measured in a pre-training phase in which 20% of the emails were phishing and compared to a similar post-training phase. The results show that the type of feedback provided during training affected participants' sensitivity to detecting subsequent phishing emails. In addition, the frequency of phishing emails during training impacted their likelihood of classifying subsequent emails as phishing or legitimate. The results of these experiments show that anti-phishing training must be carefully designed taking into consideration the impact that these design choices have on human learning and decision making.
Quantum coherence versus interferometric visibility in a biased Mach–Zehnder interferometer

The double-slit interferometer and the Mach-Zehnder interferometer (MZI) with balanced beam splitters are prototypical setups for investigating the quantum wave-particle duality. These setups induced a quantitative association of interferometric visibility (IVI) with the wave aspect of a single quantum system (WAQ). Recently, it was realized that quantum coherence (QC) can be better suited than IVI for quantifying the WAQ in complementarity relations. In this article, we investigate a MZI with biased beam splitters both in the input and the output, and we show that in some cases the IVI is not adequate to quantify the WAQ since it does not reflect the behavior of the quantum coherence, even for a bi-dimensional closed quantum system. Using IBM quantum computers, we experimentally verify our theoretical findings by doing a full quantum simulation of the optical MZI with biased beam splitters.