Reviewerly/exHarmony huggingface dataset:
Adding European Cybersecurity Skills Framework into Curricula Designer

We present the updated version of the Curricula Designer, a tool that is devoted to helping study program administrators and education providers to create cybersecurity curricula that are modern and reflect the needs of the job market. Our main contribution is the inclusion of the European Cybersecurity Skills Framework (ECSF) developed by ENISA to the Curricula Designer. The ECSF makes it possible to directly link knowledge and skills with professional profiles, which in turn reflect actual work roles on the job market. By adding ECSF to the Curricula Designer, we get a simple yet powerful tool that helps to identify the right content of cybersecurity curricula using rigorous, deterministic methods, applicable at any higher education provider. At the time of the paper submission, the Curricula Designer is the first practical application that is based on ECSF. However, due to its focus on practicality, usability and simplicity, we expect ECSF to become the dominant framework for cybersecurity knowledge and skills identification in Europe.
Sketch Transformer: Asymmetrical Disentanglement Learning from Dynamic Synthesis

Sketch-photo recognition is a cross-modal matching problem whose query sets are sketch images drawn by artists or amateurs. Due to the significant modality difference between the two modalities, it is challenging to extract discriminative modality-shared feature representations. Existing works focus on exploring modality-invariant features to discover shared embedding space. However, they discard modality-specific cues, resulting in information loss and diminished discriminatory power of features. This paper proposes a novel asymmetrical disentanglement and dynamic synthesis learning method in the transformer framework (SketchTrans) to handle modality discrepancy by combining modality-shared information with modality-specific information. Specifically, an asymmetrical disentanglement scheme is introduced to decompose the photo features into sketch-relevant and sketch-irrelevant cues while preserving the original sketch structure. Using the sketch-irrelevant cues, we further translate the sketch modality component to photo representation through knowledge transfer, obtaining cross-modality representations with information symmetry. Moreover, we propose a dynamic updatable auxiliary sketch (A-sketch) modality generated from the photo modality to guide the asymmetrical disentanglement in a single framework. Under a multi-modality joint learning framework, this auxiliary modality increases the diversity of training samples and narrows the cross-modality gap. We conduct extensive experiments on three fine-grained sketch-based retrieval datasets, i.e., PKU-Sketch, QMUL-ChairV2, and QMUL-ShoeV2, outperforming the state-of-the-arts under various metrics.
Tropical support vector machines: Evaluations and extension to function spaces

Support Vector Machines (SVMs) are one of the most popular supervised learning models to classify using a hyperplane in an Euclidean space. Similar to SVMs, tropical SVMs classify data points using a tropical hyperplane under the tropical metric with the max-plus algebra. In this paper, first we show generalization error bounds of tropical SVMs over the tropical projective torus. While the generalization error bounds attained via Vapnik–Chervonenkis (VC) dimensions in a distribution-free manner still depend on the dimension, we also show numerically and theoretically by extreme value statistics that the tropical SVMs for classifying data points from two Gaussian distributions as well as empirical data sets of different neuron types are fairly robust against the curse of dimensionality. Extreme value statistics also underlie the anomalous scaling behaviors of the tropical distance between random vectors with additional noise dimensions. Finally, we define tropical SVMs over a function space with the tropical metric.
Students’ creativity and well-being during the forced distance learning period: The correlation between variables

Introduction. The article is devoted to the study of the relationship of students’ creativity and their emotional well-being during the period of forced distance learning. It was found that students with a higher creative potential easier coped with periods of social restrictions, felt less anxiety, were better satisfied with the distance learning process, while experiencing a lack of face-to-face communication with the instructor. Aim. The aim of the current research was to identify the relationship between students’ creativity and their emotional well-being during the period of forced self-isolation in 2020–2021. Methodology and research methods . The research was carried out within the framework of health care pedagogy. The methodological basis of the study included pedagogical principles of promotion, maintenance and restoration of emotional health; development of knowledge, skills and abilities to maintain emotional health especially during online studies. Creativity was measured with the Torrance test of creative thinking; emotional state – with a well-being questionnaire created by the authors on the Google Forms platform. The study population was represented by 387 1–4-year bachelors studying the humanities (Philology, Linguistics, History, Sociology and Pedagogy) and technical sciences (Petroleum Engineering, Computer Science) at the Tyumen Industrial University, Tyumen State University and South Ural State Humanitarian and Pedagogical University. Results. Statistical analysis confirmed the hypothesis about the relationship of students’ creativity and their emotional well-being during the quarantine period. Students with higher scores on the Originality scale experienced less anxiety and stress during the quarantine period ( r = –0.36*). A negative relationship was found between the variables “Originality” and “Comfort in using PPE” ( r = –0.38*), “Development” and “Satisfaction with communication with the instructor” ( r = –0.41**), “Fluency” and “Comfort of using PPE” ( r = –0.49**). The ways of stress relief used by students during the period of self-isolation were identified and described. It was found that more than half of the students intuitively chose creative activities (61.3%) to improve their well-being. Scientific novelty . The scientific novelty lies in an attempt to present students’ creativity as a resource that requires special activation during the period of forced self-isolation within the general framework of health care pedagogy. Practical significance . Eleven forms and methods of creative interaction with students are suggested. Research findings aim at forming health preserving skills in students, especially during distance learning, by stimulating their creative resources.
Metaheuristic Assisted Hybrid Classifier for Bitcoin Price Prediction

Bitcoin has recently been greatly regarded as an investment asset. It is incredibly unpredictable despite being the biggest digital currency. Therefore, accurate forecasting is essential for making investment strategies. This is a difficulty that the latest research effort takes on to construct a revolutionary Bitcoin price prediction model by incorporating new feature engineering and price prediction methods. The original features are first retrieved from the actual Bitcoin data obtained. This work is well-fit and accurate by developing a novel feature computing framework. The proposed decomposed inter-day difference based features and the second order technical indicator are generated within the feature extraction stage. Following that, the developed two-level ensemble classifier is used to accurately forecast the Bitcoin price value using extracted and original features. The two-level ensemble classifier blends the outstanding classifiers support vector machine and artificial neural networks. It is intended to adjust the weight parameter throughout training the ensemble method to accommodate the unpredictability features of Bitcoin prices better. The article presented the novel self-adaptive bat algorithm as a solution. With regard to specific performance metrics, the output of the two-level ensemble classifier is contrasted with that of the current models.
Learn From Unpaired Data for Image Restoration: A Variational Bayes Approach

Collecting paired training data is difficult in practice, but the unpaired samples broadly exist. Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data. This work proposes LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions. Our approach is based on a carefully designed probabilistic graphical model in which the clean and corrupted data domains are conditionally independent. Using variational inference, we maximize the evidence lower bound (ELBO) to estimate the joint probability density function. Furthermore, we show that the ELBO is computable without paired samples under the inference invariant assumption. This property provides the mathematical rationale of our approach in the unpaired setting. Finally, we apply our method to real-world image denoising, super-resolution, and low-light image enhancement tasks and train the models using the synthetic data generated by the LUD-VAE. Experimental results validate the advantages of our method over other approaches.
A formal approach to identifying the impact of noise on neural networks

research-article Free AccessA formal approach to identifying the impact of noise on neural networks Authors: Iram Tariq Bhatti National University of Sciences and Technology (NUST), Islamabad, Pakistan National University of Sciences and Technology (NUST), Islamabad, PakistanView Profile , Mahum Naseer Technische Universität Wien (TU Wien), Vienna, Austria Technische Universität Wien (TU Wien), Vienna, AustriaView Profile , Muhammad Shafique New York University Abu Dhabi (NYUAD), Abu Dhabi, United Arab Emirates New York University Abu Dhabi (NYUAD), Abu Dhabi, United Arab EmiratesView Profile , Osman Hasan National University of Sciences and Technology (NUST), Islamabad, Pakistan National University of Sciences and Technology (NUST), Islamabad, PakistanView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 11November 2022 pp 70–73https://doi.org/10.1145/3550492Published:20 October 2022Publication History 1citation5,602DownloadsMetricsTotal Citations1Total Downloads5,602Last 12 Months5,602Last 6 weeks1,343 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Enabling Secure Data sharing with data deduplication and sensitive information hiding in cloud-assisted Electronic Medical Systems

Data sharing is very important for medical researchers to do research on certain diseases in cloud-assisted electronic medical systems. Nonetheless, there are large amounts of duplicate data in shared electronic medical records, which incurs redundant storage. In addition, data sharing of electronic medical records might expose the sensitive information of patients. In order to address above problems, we propose a secure data sharing scheme with data deduplication and sensitive information hiding in cloud-assisted electronic medical systems in this paper. In order to protect the sensitive information privacy and enhance the deduplication efficiency, we replace the patient's sensitive information of electronic medical records by wildcards before encrypting the whole electronic medical records. The authorized researcher can decrypt and obtain the electronic medical records under the condition that the sensitive information of shared electronic medical records is hidden. Moreover, we clarify the diagnose information of the electronic medical records into different types according to the duplicate ratio. The authorized researchers can selectively download data according to the duplicate ratio of diagnostic information. Our proposed scheme can resist brute-force attacks and single-point-of-failure attack. The experimental results show our proposed scheme is more efficient than the existing schemes.
Augmented Reality and Mixed Reality Measurement Under Different Environments: A Survey on Head-Mounted Devices

Augmented Reality (AR) and Mixed Reality (MR) have been two of the most explosive research topics in the last few years. Head-Mounted Devices (HMDs) are essential intermediums for using AR and MR technology, playing an important role in the research progress in these two areas. Behavioral research with users is one way of evaluating the technical progress and effectiveness of HMDs. In addition, AR and MR technology is dependent upon virtual interactions with the real environment. Thus, conditions in real environments can be a significant factor for AR and MR measurements with users. In this paper, we survey 87 environmental-related HMD papers with measurements from users, spanning over 32 years. We provide a thorough review of AR- and MR-related user experiments with HMDs under different environmental factors. Then, we summarize trends in this literature over time using a new classification method with four environmental factors, the presence or absence of user feedback in behavioral experiments, and ten main categories to subdivide these papers (e.g., domain and method of user assessment). We also categorize characteristics of the behavioral experiments, showing similarities and differences among papers.
Double-Deep Learning-Based Point Cloud Geometry Coding with Adaptive Super-Resolution

Point clouds represent 3D visual data in a very immersive and realistic way, offering to the users a large degree of navigation and interaction. For some key use cases, point clouds are potentially lighter and easier to acquire than other 3D representation models, thus offering an alternative with lower computational cost. To offer visual realistic and immersive experiences, notably the illusion of well-formed surfaces, point clouds typically require a large number of points. To make its storage and transmission feasible, efficient point cloud coding is essential. Recently, deep learning-based point cloud coding solutions have proven to be competitive in compression performance, excelling in distinct scenarios, although struggling to achieve similar results for sparser point clouds and lower coding rates. To tackle these limitations, this paper proposes a double-deep learning-based approach for point cloud coding by integrating a super-resolution tool. The main idea consists on converting sparser point clouds into denser ones via a down-sampling step performed before coding. Since this is a lossy process, a super-resolution step is included after decoding to mitigate the point losses and bringing the point cloud to the initial resolution. Furthermore, the sampling factor can be adaptively selected, thus offering additional flexibility to the point cloud characteristics. The proposed double-deep coding and super-resolution solution outperforms both the G-PCC Octree and V-PCC Intra point cloud coding standards achieving, respectively, 81.9% and 22.3% rate reduction measured as BD-Rate for the PSNR D1 metric.
Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-Identification

Cloth-changing person re-identification (CC-ReID), which aims to match person identities under clothing changes, is a new rising research topic in recent years. However, typical biometrics-based CC-ReID methods often require cumber-some pose or body part estimators to learn cloth-irrelevant features from human biometric traits, which comes with high computational costs. Besides, the performance is significantly limited due to the resolution degradation of surveillance images. To address the above limitations, we propose an effective Identity-Sensitive Knowledge Propagation framework (DeSKPro) for CC-ReID. Specifically, a Cloth-irrelevant Spatial Attention module is introduced to eliminate the distraction of clothing appearance by acquiring knowledge from the human parsing module. To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network. After training, the extra computations for human parsing or face restoration are no longer required. Extensive experiments show that our framework outperforms state-of-the-art methods by a large margin. Our code is available at https://github.com/KimbingNg/DeskPro.
Finger-Gesture Controlled Wheelchair with Enabling IoT

Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.
A Literature Review on the Impact of Work Environment, Leadership Styles and Resilience on Job Performance in UAE

This research reviewed literature related to the impact of work environment, leadership styles and resilence on job performance in UAE. In the curse of this review, it was figured out that, work environment tremendously affects the job performance of an employee in many ways, likewise leadership style. Both leadership style, work environment and resilience impacted on job performance in UAE based on the reviewed literature.
Genesis of Metal Sulfides and Its Significance on Graphite Mineralization in the Huangyangshan Graphite Deposit, East Junggar, Xinjiang Province, China

The Huangyangshan super-large graphite deposit is located in the Qitai area of East Junggar in Xinjiang Province, China. This deposit is well known for its distinguishing properties, including the alkaline granite complex that hosts the graphite ore, the dominantly orbicular structure developed in the graphite ore, and the association of graphite with metal sulfides in the orbicular ore. This study aims to determine the genetic relationship between graphite and metal sulfides in order to better understand the graphite mineralization process of the Huangyangshan deposit. The methods applied in the study include X-ray micro-CT scanning and scanning electron microscopy (SEM) analyses of the orbicular graphite ore and in situ inductive laser ablation-coupled plasma mass spectrometry (LA-ICP-MS) trace element analyses of the pyrrhotite and chalcopyrite associated with the graphite. The analytical results show that the graphite ore is composed of crystalline graphite, K-feldspar, albite, quartz, biotite, amphibole, and metal sulfides. The metal sulfides in the orbicular ore include pyrite, pyrrhotite, pentlandite, and chalcopyrite. According to the color, crystalline shape, texture, and occurrence, pyrrhotite can be classified into four types (I, II, III, and IV), and chalcopyrite into two types (I and II), of which types I, II, and III pyrrhotite and type I chalcopyrite have a close genetic relationship with graphite. The granular types (I, II, and III) of pyrrhotite are enriched in Co, Ni, Se, Ge, and Te and are depleted in As, Sb, Ag, and Au; they also have a high value of Co/Ni, indicating that these types of pyrrhotite have a magmatic origin. Low values of Co/Ni suggest that type IV pyrrhotite has a hydrothermal origin. The similar contents of Co and Ni and the values of Co/Ni compared with the chalcopyrite from the magmatic Co–Ni sulfide deposits imply that type I chalcopyrite has a magmatic origin. In summary, the metal sulfides of the Huangyangshan deposit are genetically related to graphite mineralization and formed predominantly by magmatic processes.
Design and evaluation of a collaborative UML modeling environment in virtual reality

Abstract Modeling is a key activity in conceptual design and system design. Through collaborative modeling, end-users, stakeholders, experts, and entrepreneurs are able to create a shared understanding of a system representation. While the Unified Modeling Language (UML) is one of the major conceptual modeling languages in object-oriented software engineering, more and more concerns arise from the modeling quality of UML and its tool-support. Among them, the limitation of the two-dimensional presentation of its notations and lack of natural collaborative modeling tools are reported to be significant. In this paper, we explore the potential of using virtual reality (VR) technology for collaborative UML software design by comparing it with classical collaborative software design using conventional devices (desktop PC/laptop). For this purpose, we have developed a VR modeling environment that offers a natural collaborative modeling experience for UML Class Diagrams. Based on a user study with 24 participants, we have compared collaborative VR modeling with conventional modeling with regard to efficiency, effectiveness, and user satisfaction. Results show that the use of VR has some disadvantages concerning efficiency and effectiveness, but the user’s fun, the feeling of being in the same room with a remote collaborator, and the naturalness of collaboration were increased.
Omission and commission errors underlying AI failures

In this article we investigate origins of several cases of failure of Artificial Intelligence (AI) systems employing machine learning and deep learning. We focus on omission and commission errors in (a) the inputs to the AI system, (b) the processing logic, and (c) the outputs from the AI system. Our framework yields a set of 28 factors that can be used for reconstructing the path of AI failures and for determining corrective action. Our research helps identify emerging themes of inquiry necessary for developing more robust AI-ML systems. We are hopeful that our work will help strengthen the use of machine-learning AI by enhancing the rates of true positive and true negative judgements from AI systems, and by lowering the probabilities of false positive and false negative judgements.
Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization

Abstract COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F 1 Score: 0.803.