Reviewerly/exHarmony huggingface dataset:
The future of open data <b>The future of open data</b> , edited by Pamela Robinson and Teresa Scassa, Ottawa, University of Ottawa Press, 2022, xii, 246 pp., CAN$69.95(hard cover), CAN$39.95(soft cover), CAN$0(PDF), CAN$29.95(epub), ISBN 9780776629742(hard cover), ISBN 9780776629735(soft cover), ISBN 9780776629759(PDF), ISBN 9780776629766(epub), open access at https://ruor.uottawa.ca/bitstream/10393/43648/1/9780776629759_WEB.pdf

"The future of open data." Journal of the Australian Library and Information Association, ahead-of-print(ahead-of-print), pp. 1–2
On Software Infrastructure: Develop, Prove, Profit?

Having infrastructure survive over very long stretches of time is a nontrivial task. This is either because such infrastructure needs to be built extremely well from the outset or because it requires ongoing maintenance. The former may require prohibitively large initial investments. The latter requires ongoing investment from public agencies over the span of decades or centuries despite the pendulum swings of those governments from contrasting political aims. Without either the very high initial or ongoing investment, infrastructure can fail. Physical infrastructure failure is not inevitable—consider railways in Japan, the Panama Canal, and the U.S. Interstate Highway System.
Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.
On Applying Expert Knowledge for Spline-Based Segmentation of Liver on Computed Tomography Images

The segmentation of the liver from the surrounding soft tissues between the ribs on a non-enhanced, arterial or delayed phase computed tomography image complicated, as the muscle and many other soft tissues have very similar X-ray attenuation to the liver, thus they manifest as similar intensity and similar pattern domains on the computed tomography images. However, if the anatomical setup of the human body and the location of the bones, that have much higher attenuation are taken into account, a sufficiently smooth and precise borderline can be drawn between the ribcage and the liver, that can be developed into a liver contour by successive erosion and active contour methods.
Data Protection and Recovery Performance Analysis of Cloud-Based Recovery Service

An analytical modeling approach is made to the MARS cloud service. The analysis is based on a real system placed in a production environment with real influences from the rest of the IT infrastructure. Based on the results obtained from the operation of the system, a model is created that enables for service system analysis through the assessment of the values of a number of parameters for certain data protection scenarios.
Anomaly Prediction over Human Crowded Scenes via Associate-Based Data Mining and K-Ary Tree Hashing

Anomaly detection and behavioral recognition are key research areas widely used to improve human safety. However, in recent times, with the extensive use of surveillance systems and the substantial increase in the volume of recorded scenes, the conventional analysis of categorizing anomalous events has proven to be a difficult task. As a result, machine learning researchers require a smart surveillance system to detect anomalies. This research introduces a robust system for predicting pedestrian anomalies. First, we acquired the crowd data as input from two benchmark datasets (including Avenue and ADOC). Then, different denoising techniques (such as frame conversion, background subtraction, and RGB-to-binary image conversion) for unfiltered data are carried out. Second, texton segmentation is performed to identify human subjects from acquired denoised data. Third, we used Gaussian smoothing and crowd clustering to analyze the multiple subjects from the acquired data for further estimations. The next step is to perform feature extraction to multiple abstract cues from the data. These bag of features include periodic motion, shape autocorrelation, and motion direction flow. Then, the abstracted features are mapped into a single vector in order to apply data optimization and mining techniques. Next, we apply the associate-based mining approach for optimized feature selection. Finally, the resultant vector is served to the k-ary tree hashing classifier to track normal and abnormal activities in pedestrian crowded scenes.
Deep Image Inpainting to Support Endoscopic Procedures

Deep image inpainting is a computer vision task that uses Deep Neural Networks to generate plausible content to complete an image, for example for the restoration of a damaged image or the removal of unwanted elements captured in the picture. This paper uses deep image inpainting to restore endoscopic images that are affected by various types of artifacts. To this end, we developed a transfer learning-based procedure that uses the CSA inpainting model, which was originally proposed for unrelated tasks including the restoration of images from the Paris StreetView Dataset. The proposed system is trained and validated on the EndoCV2020 dataset, consisting of images from real endoscopies, highlighting how deep image inpainting may be a promising technology for frame restoration during medical procedures.
The Current State of Security Governance and Compliance in Large-Scale Agile Development: A Systematic Literature Review and Interview Study

Agile methodologies have gained popularity in software and information systems engineering due to their ability to enable rapid adaption to changing requirements and ensure business value creation in fast-paced environments. However, scaling agile to multiple teams presents challenges related to security governance and compliance. Traditional security activities struggle to keep pace with iterative agile methods. The tension between security and agility intensifies in scaled environments as governance and compliance procedures conflict with the desired autonomy of agile teams. With the increase in the number and complexity of security risks, it is imperative to better understand the current challenges and solution approaches for security governance in large-scale agile development (LSAD). To this end, we conducted a systematic literature review and an interview study involving nine industry experts. We identified 15 relevant challenges and analyzed existing LSAD frameworks concerning their solution approaches for achieving security governance and compliance. In addition, we contribute an overview of alternative solution approaches and propose five factors to balance control and autonomy to mitigate security challenges in LSAD. Our findings provide a foundation for developing well-grounded solution artifacts that address the identified challenges,
Intelligent service for monitoring the activities of an employee of an organization

This article presents an architecture for an intelligent service aimed at monitoring employee working hours in an organization. The proposed architecture is flexible, scalable, and secure, offering tools for in-depth analysis of employee activities to identify areas for improvement and optimization. By implementing this architecture, organizations can effectively track employee working hours, identify inefficient use of work time, and uncover problem areas, ultimately leading to increased productivity, cost reduction, and an improved work environment. The intelligent service modules described function as a powerful tool for workflow optimization and enhanced employee productivity. The developed client application, “Screenshot of the Employee's Computer Screen,” facilitates automatic data collection from employee workstations, providing ample information for activity analysis. This data is then processed using an intelligent model to assess work productivity accurately and identify potential problems or areas for improvement. The article also discusses the implementation of an ETL process in the first version of the model for processing data from screenshots and the Development of a function that establishes a correspondence between the got and reference set of keywords. Overall, the intelligent service for monitoring employee working hours promises to automate personnel management processes, enhance work time data accuracy and reliability, and promote more efficient use of company resources.
Optical and SAR Images Matching Based on Phase Structure Convolutional Features

High-precision image matching techniques are required to fully utilize complementary information from optical and synthetic aperture radar (SAR) images. However, there are notable nonlinear radiometric differences (NRD) between optical and SAR images because of the various imaging techniques used by the various sensors. The existing template matching method based on the Siamese structure underutilizes the phase structure information, which is less susceptible to NRD, resulting in subpar matching precision. To address this problem, this letter proposes an optical and SAR image matching method based on phase structure convolutional features that use the log-Gabor filter to extract the multi-orientation phase structure information of the image. It constructs a multi-scale fusion SiamUNet-7 (MSF SiamUNet-7) network to extract the phase structure convolutional features to fully fuse the local texture information at a large scale and the global structure information at a small scale. The phase structure convolutional features of the optical and SAR images are used to generate the image pair similarity map using the mutual correlation layer, and the peak position in the similarity map is regarded as the best matching result. Experiments showed that, on the cropped Tiny-SEN1-2 dataset, the correct matching rate (CMR) and mean matching error (mME) of the threshold <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> ≤ 4 of the proposed method were 92.24% and 1.348, respectively, which improved the CMR( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> ≤ 4) by 4.51% and reduced the mME by 0.046 compared with the original SiamUNet-9 model. The proposed method can effectively overcome the large NRD between the optical and SAR images and achieve high-precision matching.
Towards User-Centered Privacy Adaptation Management: Insights From Privacy Research and a System-of-Systems Architecture

The integration of, and the interaction between, elements and processes of the physical world and the cyber world lie at the core of Cyber-Physical Systems (CPS). Their complexity and heterogeneity make such systems often difficult to grasp for stakeholders and to control their development. Especially with regard to privacy, it is necessary for end-users and developers to understand the system's architecture and its behavior. In particular, users actively caring about privacy need to know how their data is affected by operating a CPS and how they can manage and protect it. Due to the technology-driven architecture of CPS, design approaches need to encompass privacy management functionality that can be adapted to individual scenarios. We propose a System-of-Systems architecture taking into account existing concepts from privacy management fields, including Privacy-by-Design and Privacy Languages. Each CPS component can be accessed via a privacy management interface that guides stakeholders to configure a CPS according to privacy requirements.
VR VanMieu: The Interactively Virtual Temple of Literature

Historical heritage is representative stowage for our memories and provides us with a physical means to connect to past values. Recently, digital preservation of heritage has attracted many researchers. Virtual reality (VR) technology is the ability to simulate a complete and interactive virtual environment for audiences to get a more comprehensive and concrete presentation. In this paper, we propose a process of building a VR VanMieu application - a virtual heritage environment using VR technology to promote historical knowledge of the Temple of Literature.
Another Look at Cybersecurity Awareness Programs

Cybercrime has become one of the biggest forms of crime in the world today – if not the biggest form. Everybody is seeking ways to address this growing cyber risk. Cybersecurity awareness of end users is an important component of helping to prevent cybercrime. However, research indicates that traditional cybersecurity awareness programs are not very successful. Budgets for cyber protection programs keep increasing, but there is no evidence that the levels of cybercrime are decreasing. Companies (across the globe) are searching for new ways and approaches to make their end users more cyber aware. What has become clear from many efforts and approaches in making end users cyber aware, is that an approach emphasizing the technical aspects alone does not work. A complementary human oriented approach is also needed. This paper advances 3 new possible approaches which can be considered in the challenge to create more cyber aware end users. The first approach, called the ‘Fighter’ approach, is taken from the area of firefighting, where employees are trained to fight a fire in an emergency. The second approach, called the ‘Ownership’ approach, is from the operational technology (OT) area where machine operators are trained to take ownership of their machines and safely operate their machines. The third approach, called the ‘Workplace’ approach, is taken from the area of workplace training where being cyber-awareness is seen as a part of a secure workplace. All three these approaches are based on primarily on letting the end user realise that cybersecurity awareness is actually part of their daily job environment.
Multi-modal with Multiple Image Filters for Facial Emotion Recognition

The need to understand people, especially their behaviors and feelings, is growing significantly in today’s quickly-moving world. Despite the remarkable progress of science and technology in general and artificial intelligence in particular, facial emotion recognition remains challenging. This paper proposes a unique method for enhancing the accuracy of emotion recognition models. Through image analysis, the hair area and other facial areas have similar pixels but different intensities. However, to recognize emotions on the face, people only need to focus on facial features. Therefore, areas with the same pixels are not very helpful in accurately recognizing emotions. To solve the above problem, we conducted to eliminate or blur pixels that are the same as on the facial image. In addition, we also demonstrate that using the multi-model approach can support the learning process by allowing the sub-models to collaborate and increase accuracy. The experiments showed that the proposed approach offers a valuable contribution to the field of facial emotion recognition and has a significant improvement compared to previous approaches.
Cyber Range Exercises: Potentials and Open Challenges for Organizations

The shortage of skilled cybersecurity professionals poses a significant challenge for organizations seeking to protect their assets and data. To address this shortage, onboarding and reskilling employees for cybersecurity positions becomes a daunting task for organizations. Cyber ranges mirror digital infrastructures to provide a realistic yet safe environment for cybersecurity training. To date, the potential of cyber ranges has been leveraged primarily in academic education. This paper investigates how cyber range exercises (CRX) can enhance the onboarding and reskilling of cybersecurity professionals in organizations. To this end, we conducted semi-structured interviews with seven cybersecurity professionals from organizations in different industry sectors in Germany and India. Our findings indicate that the main potential of CRXs lies in conveying universal cybersecurity concepts that are transferable to the particular systems, technologies and tools of an organization. Thereby, CRXs represent a promising complement to existing organizational training strategies. Challenges to overcome were identified in establishing an organizational CRX infrastructure, building the necessary competencies to conduct the exercises, and ensuring the comparability of CRXs to validate personal competence development.
Introduction

Chronicles have been studied in the context of two analysis problems for temporal sequences: recognizing situations in temporal sequences and abstracting a set of temporal sequences. In the former problem, a chronicle is the description of a situation to be recognized in a temporal sequence. The main application field is the monitoring of system behaviour. Given a set of temporal sequences, the latter problem consists in building a chronicle that generalizes them. Such a task pertains to knowledge discovery: Chronicles then amount to synthetic pieces of information that are meant to serve as an abstraction of the data. In this chapter, we introduce the notion of a chronicle and we develop a natural interpretation for it with respect to these two problems. We offer some intuition about our choice of a representation and go into more detail on a few technical peculiarities.
Robust and unbiased positive and unlabeled learning

Positive and unlabeled learning (PU learning) is a highly effective classification method that only involves positive and unlabeled samples. It has garnered significant attention in recent years due to its ability to greatly improve classification performance when negative samples are absent. However, numerous contributions in this field remain theoretical and fail to perform well on actual data polluted by noise. How to deal with these challenges is currently a hot research topic. Inspirited by the previous work, we propose a noise-insensitive and unbiased classification method to deal with noise data, i.e., Pinball Loss Factorization and Centroid Smoothing, called Pin-LFCS. For nonlinear situation, we also present an extended kernelized version named Pin-KLFCS. Theoretically, we investigate the property of our proposed methods, including noise insensitivity and intra-class scatter minimization. Moreover, generalization error bounds are derived to ensure their validity. Experimentally, through extensive experiments on 14 benchmark datasets with varying levels of noise, we demonstrate the efficacy and validity of our proposed methods, which outperform the existing advanced methods.
Experience of developing the mentoring system in APOU UR «RMC MH UR»

The article presents the experience of implementing network mentoring among teachers in the APOU UR «RMC MH UR». The development of the network format of mentoring is caused by the need to organize the exchange of experience of teachers who are successful in certain types of teaching activities, and their desire to train other motivated teachers.
Semi-Asynchronous Model Design for Federated Learning in Mobile Edge Networks

Federated learning (FL) is a distributed machine learning (ML). Distributed clients train locally and exclusively need to upload the model parameters to learn the global model collaboratively under the coordination of the aggregation server. Although the privacy of the clients is protected, which requires multiple rounds of data upload between the clients and the server to ensure the accuracy of the global model. Inevitably, this results in latency and energy consumption issues due to limited communication resources. Therefore, mobile edge computing (MEC) has been proposed to solve communication delays and energy consumption in federated learning. In this paper, we first analyze how to select the gradient values that help the global model converge quickly and establish theoretical analysis about the relationship between the convergence rate and the gradient direction. To efficiently reduce the energy consumption of clients during training, on the premise of ensuring the local training accuracy and the convergence rate of the global model, we adopt the deep deterministic policy gradient (DDPG) algorithm, which adaptively allocates resources according to different clients' requests to minimize the energy consumption. To improve flexibility and scalability, we propose a new semi-asynchronous federated update model, which allows clients to aggregate asynchronously on the server, and accelerates the convergence rate of the global model. Empirical results show that the proposed Algorithm 1 not only accelerates the convergence speed of the global model, but also reduces the size of parameters that need to be uploaded. Besides, the proposed Algorithm 2 reduces the time difference caused by users heterogeneity. Eventually, semi-asynchronous update model is better than synchronous update model in communication time.
Unsupervised machine learning-based prospectivity analysis of NW and NE India for carbonatite-alkaline complex-related REE deposits

Rare Earth Elements (REE) form critical raw materials in environment-friendly, high-technology devices and components, and therefore have been classified as “critical minerals and metals” by most countries. About 62% of the global resources of REE occur associated with carbonatite-alkaline complexes; however, the entire production of REE in India currently comes from secondary deposits, even though India contains a variety of REE-enriched primary source rocks, particularly carbonatites and alkaline complexes. There is, therefore, a significant potential in the county for new REE deposit discoveries associated with carbonatite-alkaline complexes. This research attempts to identify exploration targets for REE associated with carbonatite-alkaline complexes in northern India utilising a Self-Organising Maps (SOM)-driven workflow. This unsupervised machine-learning-based workflow eliminates the hand-crafting of input predictor features. The algorithm creates clusters of features directly from primary gridded geophysical and topographical datasets. The obtained clusters are then analysed based on available geological knowledge and empirical spatial associations with known occurrences in the study areas to identify prospective clusters and generate prospectivity maps. Nine new targets are identified across the Shillong plateau in northeastern and Western Rajasthan in northwestern India. These new targets, in addition to the known carbonatite-alkaline complexes, are recommended for further data collection and follow-up exploration. It is noteworthy that these targets conform to the targets identified by Aranha et al. (2022a, 2022b) using mineral systems-guided fuzzy inference systems.