Reviewerly/exHarmony huggingface dataset:
Model-based cloud service deployment optimisation method for minimisation of application service operational cost

Abstract Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.
Existence and Uniqueness of Algebraic Closures

Summary This is the second part of a two-part article formalizing existence and uniqueness of algebraic closures, using the Mizar [2], [1] formalism. Our proof follows Artin’s classical one as presented by Lang in [3]. In the first part we proved that for a given field F there exists a field extension E such that every non-constant polynomial p ∈ F [ X ] has a root in E . Artin’s proof applies Kronecker’s construction to each polynomial p ∈ F [ X ] \F simultaneously. To do so we needed the polynomial ring F [ X 1 , X 2 , ...] with infinitely many variables, one for each polynomal p ∈ F [ X ] \F . The desired field extension E then is F [ X 1 , X 2 , …] \I , where I is a maximal ideal generated by all non-constant polynomials p ∈ F [ X ]. Note, that to show that I is maximal Zorn’s lemma has to be applied. In this second part this construction is iterated giving an infinite sequence of fields, whose union establishes a field extension A of F , in which every non-constant polynomial p ∈ A [ X ] has a root. The field of algebraic elements of A then is an algebraic closure of F . To prove uniqueness of algebraic closures, e.g. that two algebraic closures of F are isomorphic over F , the technique of extending monomorphisms is applied: a monomorphism F → A , where A is an algebraic closure of F can be extended to a monomorphism E → A , where E is any algebraic extension of F . In case that E is algebraically closed this monomorphism is an isomorphism. Note that the existence of the extended monomorphism again relies on Zorn’s lemma.
Methods and tools for healthcare data anonymization: a literature review

AbstractHealthcare is a rapidly evolving field. Such development creates opportunities to provide better quality, evidence-based treatment, however, increasing privacy violations. Anonymization can be applied to share data safely. This paper is a literature review of methods and tools for anonymization that includes 1930 papers, 32 of which were selected for the final evaluation. This article is an updated and extended version of the research conducted by our team in 2020 (Vovk 2021a). We found that despite the variety of methods, there is no single method that fits all cases. Certain methods are more widely used and create a background for advanced and secure methods. Our research shows that anonymization methods are used in tools for simplification and automation. Among challenges related to the use of all anonymization methods, is the proper balance between risk and data utility and highlight that the anonymization process is not limited to only technical measures but interdisciplinary.KEYWORDS: Anonymizationhealthcare datastructured datamethodstools Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been conducted as part of the project "ICT programme", which was supported by the European Union through the European Social Fund (under Grant SFOS nr 2014-2020.4.05.19-001).
A modeling language for novice engineers to design well at SaaS product companies

Software-as-a-Service (SaaS) product companies have brought in significant changes in how we build software from architecture and engineering process perspective. SaaS products are large, distributed software systems hosted in cloud and built using collaborating services (or micro-services). The software releases happen in days and weeks, necessitating an agile development process. Novice engineers (those who join the company fresh from college) need to become comfortable with complex systems and proficient in agile delivery with high quality, otherwise they fall behind in productivity.
An anti-interference dynamic integral neural network for solving the time-varying linear matrix equation with periodic noises

In order to solve a time-varying linear matrix equation with periodic noises, an anti-interference dynamic integral neural network (AI-DINN) is proposed. Based on an indefinite unbounded vector/matrix-type error function, the proposed AI-DINN includes an integral structure, a recursive structure, and an adjustment module. It has the excellent ability to overcome the interference of periodic noises. This paper theoretically proves the convergence and robustness of the proposed AI-DINN for solving the time-varying linear matrix equation with the interference of periodic noises. Computer simulation results verify that the proposed AI-DINN method based on different activation functions can achieve convergence within limited time with the interferences of different periodic noises. In addition, the proposed AI-DINN with different activation functions has its own advantages with the interference of different types of periodic noises. Furthermore, comparative simulation experiments verify that the proposed AI-DINN has better convergence and anti-interference performance compared with state-of-the-art methods.
A study of transfer of information in animal collectives using deep learning tools

We studied how the interactions among animals in a collective allow for the transfer of information. We performed laboratory experiments to study how zebrafish in a collective follow a subset of trained animals that move towards a light when it turns on because they expect food at that location. We built some deep learning tools to distinguish from video which are the trained and the naïve animals and to detect when each animal reacts to the light turning on. These tools gave us the data to build a model of interactions that we designed to have a balance between transparency and accuracy. The model finds a low-dimensional function that describes how a naïve animal weights neighbours depending on focal and neighbour variables. According to this low-dimensional function, neighbour speed plays an important role in the interactions. Specifically, a naïve animal weights more a neighbour in front than to the sides or behind, and more so the faster the neighbour is moving; and if the neighbour moves fast enough, the differences coming from the neighbour's relative position largely disappear. From the lens of decision-making, neighbour speed acts as confidence measure about where to go. This article is part of a discussion meeting issue 'Collective behaviour through time'.
Multi-Tier Reputation for Data Cooperatives

Data cooperatives allow their members—the data owners—to pool their digital assets together for processing and access management. In this context, reputation is an important measure of trust, which can effectively complement financial assets in the decentralized scenario, also providing incentives for users’ honest behavior. We present a decentralized data cooperative system based on the Proof-of-Reputation and Proof-of-Stake blockchains. In order to provide inclusivity for low-reputation (newly joined) users, which is required in our community-based scenario, we use the tier-based committee selection introduced by Kleinrock et al. at Indocrypt 2020. As the underlying Proof-of-Stake system, we use Snow White due to its convenient properties such as flexible committee selection and user participation.
A Code Centric Evaluation of C/C++ Vulnerability Datasets for Deep Learning Based Vulnerability Detection Techniques

Recent years have witnessed tremendous progress in NLP-based code comprehension via deep neural networks (DNN) learning, especially Large Language Models (LLMs). While the original application of LLMs is focused on code generation, there have been attempts to extend the application to more specialized tasks, like code similarity, author attribution, code repairs, and so on. As data plays an important role in the success of any machine learning approach, researchers have also proposed several benchmarks which are coupled with a specific task at hand.
Judging emoji by occupation: A case of emoji-based sarcasm interpretation

The smiling emoji has been claimed to be a marker of sarcastic intention among young Chinese users in computer-mediated communication. However, it is not well understood whether people interpret the emoji differently based on the characteristics or traits of the sender, as conveyed by occupation stereotypes. We investigated the effect of sender occupation on emoji-based sarcasm interpretation in both unambiguous (Experiment 1) and ambiguous (Experiment 2) contexts. The results showed that contextual incongruity was privileged over sender occupation in cueing sarcastic intention. In unambiguous contexts, sender occupation exerted no significant influence on the interpretation of emoji-based sarcastic statements. In contrast, sender occupation played an important role in the interpretation of emoji-based statements in ambiguous contexts. Specifically, emoji-based ambiguous statements delivered by senders in high‑irony occupations were more likely to be perceived as sarcastic than by those in low-irony occupations. However, sender occupation did not affect the interpretation of the emoji; instead, it biased the judgment of emoji in sarcasm interpretation. In a follow-up experiment (Experiment 3), we investigated the perceived characteristics of both high- and low-irony occupations. The results demonstrated that individuals in high-irony occupations were stereotyped with characteristics, including being humorous, insincere, easy to setting up close relationships, and of a lower social status. Taken together, our study suggests that stereotypical information about the sender could drive the interpretation of potentially sarcastic statements, and the contextual information modulates the effect of sender occupation on sarcasm interpretation.
Learning Mealy machines with one timer

We present Mealy machines with a single timer (MM1Ts), a class of sufficiently expressive models to describe the real-time behavior of many realistic applications that we can learn efficiently. We show how we can obtain learning algorithms for MM1Ts via a reduction to the problem of learning Mealy machines. We describe an implementation of an MM1T learner on top of LearnLib and compare its performance with recent algorithms proposed by Aichernig et al. and An et al. on several realistic benchmarks.
Category-aware feature attribution for Self-Optimizing medical image classification

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image classification. Although remarkable advancement has been made, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the unavoidable hard samples and minority class samples. Meanwhile, it is hard to understand the prediction mechanism and debug the wrong prediction. To address these issues, this paper proposes a Self-Optimizing medical image classification framework based on category-aware feature attribution, which automatically performs model diagnosis, repairing, and enhancement. On one hand, we introduce selective filter restraining to alleviate the disturbance of misclassified features for hard samples. On the other hand, we introduce sample-specific fine-tuning to promote category-wise feature learning, especially for minority classes. The Self-Optimizing framework is a versatile module for other medical image classifiers based on mainstream CNN structures. We extensively validated the proposed method on three typical yet challenging medical image classification tasks: (i) skin lesion classification from dermoscopic images in the International Skin Imaging Collaboration (ISIC) 2019 dataset, (ii) breast cancer classification from ultrasound images in Breast Ultrasound Images (BUSI) dataset, and (iii) breast tumour classification from histopathologic scans of lymph node sections in PatchCamelyon (PCam) dataset. Experimental results demonstrate that the proposed framework achieves 1%∼5% performance gain for various classifiers, brings better explainability, and can be applied to incremental learning scenarios.
Postgraduate Students’ Perspectives on Mobile Technology Benefits and Learning Possibilities: Insights from Greek Students

University students increasingly use mobile devices in their studies, while during the COVID-19 pandemic, mobile technology supported learning was applied by many students. Exploring students’ mobile technology perceptions is an ongoing research issue. This pilot study aimed to explore postgraduate students’ perspectives on the benefits and learning possibilities of mobile devices’ usage in their studies. In total, 34 Greek postgraduate students participated, and they answered an online questionnaire. The students expressed positive, strong perceptions, indicating their awareness of mobile technology learning possibilities and educational benefits for their studies. Almost all students (94.1%) agreed that mobile technology should be used to connect postgraduate students with people, content, and resources, and that mobile devices bring new learning opportunities in a postgraduate programme. Isolated significant differences were observed with regard to age and postgraduate mode of study (more positive views were expressed for blended mode in comparison to face-to-face mode). Implications for tutors, educational practices, and university policies are discussed.
Savior or Distraction for Survival: Examining the Applicability of Machine Learning for Rural Family Farms in the United Arab Emirates

Machine learning (ML) has seen a substantial increase in its role in improving operations for staff and customers in different industries. However, there appears to be a somewhat limited adoption of ML by farm businesses, highlighted by a review of the literature investigating innovative behaviors by rural businesses. A review of the literature identified a dearth of studies investigating ML adoption by farm businesses in rural regions of the United Arab Emirates (UAE), especially in the context of family-owned farms. Therefore, this paper aims to investigate the drivers and barriers to ML adoption by family/non-family-owned farms in rural UAE. The key research questions are (1) what are the drivers and barriers for rural UAE farms adopting ML? As well as (2) is there a difference in the drivers and barriers between family and non-family-owned farms? Twenty semi-structured interviews were conducted with farm businesses across several rural regions in the UAE. Then, through a Template Analysis (TA), drivers and barriers for rural UAE-based farm owners adopting ML were identified. Interview findings highlighted that farms could benefit from adopting ML in daily operations to save costs and improve efficiency. However, 16 of 20 farms were unaware of the benefits related to ML due to access issues (highlighted by 12 farms) in incorporating ML operations, where they felt that incorporating ML into their operations was costly (identified by 8 farms). It was also identified that non-family-owned farms were more likely to take up ML, which was attributed to local culture influencing family farms (11 farms identified culture as a barrier). This study makes a theoretical contribution by proposing the Machine Learning Adoption Framework (MLAF). In terms of practical implications, this study proposes an ML program specifically targeting the needs of farm owners in rural UAE. Policy-based implications are addressed by the findings aligning with the United Nations’ Sustainability Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities).
Relationship between Cyber Security and Civil Protection in the Greek Reality

The links between cybersecurity and civil protection in the last decades became strong due to the rapid increase in the use of Informatics Technologies (IT) worldwide and the need to increase citizens’ protection from various disasters and uncertainties. The objective of the present study is to explore the role of cyber security in enhancing civil protection in Greek reality. Methodologically, the study has been based on a survey with a sample of 345 executives of IT companies in Greece by using a structured questionnaire. The basic results of the research study showed that cyber security technologies positively affect civil protection, cybercrime reduction practices have a positive influence on civil protection, and there is a significant relationship between the government’s role in cyber security and civil protection. Cybersecurity also may have a significant influence on the principles of emergency operations: prevention, mitigation, preparation, response, or emergency evacuation and recovery. The main conclusion from the study is that it is very important for public and private agencies in Greece, as well as in the rest of Europe, to embrace new emerging cybersecurity technologies to help enhance cyber security and civil protection. Potential vulnerabilities in cybersecurity pose substantial risks to the effectiveness and efficiency of such critical infrastructure and directly impact the functioning of states, economies, and societies.
Multi-Step Structure Image Inpainting Model with Attention Mechanism

The proliferation of deep learning has propelled image inpainting to an important research field. Although the current image inpainting model has made remarkable achievements, the two-stage image inpainting method is easy to produce structural errors in the rough stage because of insufficient treatment of the rough inpainting stage. To address this problem, we propose a multi-step structured image inpainting model combining attention mechanisms. Different from the previous two-stage inpainting model, we divide the damaged area into four sub-areas, calculate the priority of each area according to the priority, specify the inpainting order, and complete the rough inpainting stage several times. The stability of the model is enhanced by the multi-step method. The structural attention mechanism strengthens the expression of structural features and improves the quality of structure and contour reconstruction. Experimental evaluation of benchmark data sets shows that our method effectively reduces structural errors and improves the effect of image inpainting.
Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.
Developing a near Real-Time Cloud Cover Retrieval Algorithm Using Geostationary Satellite Observations for Photovoltaic Plants

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require many auxiliary data and computing resources, which are hard to implement or transplant for applications at PV plants. In this study, a portable and fast cloud mask algorithm (FCMA) is developed to provide near real-time (NRT) spatial-temporally matched cloud cover products for PV plants. The geostationary satellite imager data from the Advanced Himawari Imager aboard Himawari-8 and the related operational cloud mask algorithm (OCMA) are employed as benchmarks for comparison and validation. Furthermore, the ground-based manually observed cloud cover data at seven quintessential stations at 08:00 and 14:00 BJT (Beijing Time) in 2017 are employed to verify the accuracy of cloud cover data derived from FCMA and OCMA. The results show a high consistency with the ground-based data, and the average correlation coefficient (R) is close to 0.85. Remarkably, the detection accuracy of FCMA is slightly higher than that of OCMA, demonstrating the feasibility of FCMA for providing NRT cloud cover at PV plants.