Reviewerly/exHarmony huggingface dataset:
shARe-IT: Ad hoc Remote Troubleshooting through Augmented Reality

Abstract 10 years ago, Castellani et al. (Journal of Computer Supported Cooperative Work, vol. 18, no. 2–3, 2009, pp. 199–227, 2009) showed that using just an audio channel for remote troubleshooting can lead to a range of problems and already envisioned a future in which augmented reality (AR) could solve many of these issues. In the meantime, AR technologies have found their way into our everyday lives and using such technologies to support remote collaboration has been widely studied within the fields of Human-Computer Interaction and Computer-Supported Cooperative Work. In this paper, we contribute to this body of research by reporting on an extensive empirical study within a Fab Lab of troubleshooting and expertise sharing and the potential relevance of articulation work to their realization. Based on the findings of this study, we derived design challenges that led to an AR-based concept, implemented as a HoloLens application, called shARe-it. This application is designed to support remote troubleshooting and expertise sharing through different communication channels and AR-based interaction modalities. Early testing of the application revealed that novel interaction modalities such as AR-based markers and drawings play only a minor role in remote collaboration due to various limiting factors. Instead, the transmission of a shared view and especially arriving at a shared understanding of the situation as a prerequisite for articulation work continue to be the decisive factors in remote troubleshooting.
APPLICATION OF DIGITAL INFORMATION RESOURCES IN DISTANCE LEARNING

The article shows that the introduction of distance learning courses, trainings, webinars, workshops, round tables, etc. at a more systematic level are the effective ways of forming digital literacy. Possibilities of using distance learning in higher education institutions are considered. The article emphasizes that digitization affects not only the content of education but also its organization. These processes have mixed implications for positioning of universities and teaching work. Digitalization of education opens up new perspectives for any professional field, as it enables to expand the context of activities, organize international cooperation, create virtual groups and communities, forums and chats, provide access to materials of various formats (texts, multimedia, television, etc.), expand communication with native speakers through various Internet systems, etc. The advent of distance education has led to a whole new understanding of the learning process and has expanded its capabilities. The introduction of distance learning in Ukraine is currently in its active stage. At the beginning of the XXI century The Concept of Development of Distance Education in Ukraine and the Regulation on Distance Learning were approved. Today, the role and importance of digital educational resources and technologies in the educational sector is increasing; a great deal of efforts are being made to harmonize the structure of national HEIs with European requirements and to improve the quality of distance learning, which should lead to an increase in the level of training of specialists. Distance education is an important component of modern specialist training in many countries of the world, based on the use of both the best traditional teaching methods and new DER, as well as on the principles of self-study, intended for the general public irrespective of material security, place of residence, health status. A distinctive feature of distance education is interactive learning. Students have the opportunity not to attend classes, but to study at convenient time and in a convenient place. The main goal of distance learning is to ensure further development of the education system towards providing it with features and characteristics of open education (accessibility, mass, personal orientation, real continuityetc). Thus, distance education is a 21st century specialist training system, characterized by high professionalism that strives for collaboration, self-affirmation and a high level of communication with colleagues. It is characterized by a great intensification of socially significant motives: business, cognitive, cooperation, self-realization and development, self-affirmation and communication through the use of open and computer-based learning and modern means of communication.
Leveraging graph-based hierarchical medical entity embedding for healthcare applications

Abstract Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare data mining that turns heterogeneous medical records into structured and actionable information. Here we propose , an algorithmic framework for learning continuous low-dimensional embedding vectors of the most common entities in EHR: medical services, doctors, and patients. features a hierarchical structure that encapsulates different node embedding schemes to cater for the unique characteristic of each medical entity. To embed medical services, we employ a biased-random-walk-based node embedding that leverages the irregular time intervals of medical services in EHR to embody their relative importance. To embed doctors and patients, we adhere to the principle “it’s what you do that defines you” and derive their embeddings based on their interactions with other types of entities through graph neural network and proximity-preserving network embedding, respectively. Using real-world clinical data, we demonstrate the efficacy of over competitive baselines on diagnosis prediction, readmission prediction, as well as recommending doctors to patients based on their medical conditions. In addition, medical service embeddings pretrained using can substantially improve the performance of sequential models in predicting patients clinical outcomes. Overall, can serve as a general-purpose representation learning algorithm for EHR data and benefit various downstream tasks in terms of both performance and interpretability.
Analysis of Ring-Oscillator-based True Random Number Generator on FPGAs

In this paper, randomness of ring-oscillator-based True Random Number Generators (TRNGs) on Field Programmable Gate Arrays (FPGAs) are analyzed according to the number of constituent inverters and the number of ring oscillators. A typical ring oscillator is composed of an odd number of inverter chain, in which the output of the last inverter is fed back to the input of the first inverter. TRNG utilizes a different jitter caused by PVT variations as an entropy source. To evaluate ring-oscillator-based TRNG on FPGAs, we implement various TRNGs with different numbers of constituent inverters and ring oscillators on Xilinx Spartan-3 FPGA, and the randomness is evaluated by the NIST SP 800-22 test. According to experimental results, it shows that TRNG with the smaller number of constituent inverters and the larger number of ring oscillators provides the stronger randomness.
Image segmentation by using K-means clustering algorithm in Euclidean and Mahalanobis distance calculation in camouflage images

In camouflage images, the texture of an object is hidden in the background image texture. The hidden object has almost the same color tone and texture as the background image. Since camouflage images show close features with background texture, it is quite difficult to segment and detect the camouflaged object from the image background. In this study, image segmentation is performed on camouflage images using K-means method using Euclidean and Mahalanobis distance calculations. The average value of RMSE 262.47 and the average value of PSNR 24.26 was obtained when using Euclidean distance calculation. Also, the average value of RMSE 799.62 and the average value of PSNR 19,66 was obtained when using Mahalanobis distance calculation. According to the result obtained from this study, while the low RMSE values were obtained with the K-means method by using the Euclidean distance calculation, the lower PSNR values were obtained by using the Mahalanobis distance calculation. In the experimental results; K-means method with Euclidean distance calculation is more successful than the K-means method with Mahalanobis distance calculation.
Cyber Range Automation Overview with a Case Study of CRATE

Abstract Cyber security research is quintessential to secure computerized systems against cyber threats. Likewise, cyber security training and exercises are instrumental in ensuring that the professionals protecting the systems have the right set of skills to do the job. Cyber ranges provide platforms for testing, experimentation and training, but developing and executing experiments and training sessions are labour intensive and require highly skilled personnel. Several cyber range operators are developing automated tools to speed up the creation of emulated environments and scenarios as well as to increase the number and quality of the executed events. In this paper we investigate automated tools used in cyber ranges and research initiatives designated to augment cyber ranges automation. We also investigate the automation features in CRATE (Cyber Range And Training Environment) operated by the Swedish Defence Research Agency (FOI).
Dialogue System Live Competition: Identifying Problems with Dialogue Systems Through Live Event

We organized a competition entitled “the dialogue system live competition” in which the audience, consisting mainly of researchers in the dialogue community, watched and evaluated a live dialogue conducted between users and dialogue systems. The motivation behind the event was to cultivate state-of-the-art techniques in dialogue systems and enable the dialogue community to share the problems with current dialogue systems. There are two parts to the competition: preliminary selection and live event. In the preliminary selection, eleven systems were evaluated by crowd-sourcing. Three systems proceeded to the live event to perform dialogues with designated speakers and to be evaluated by the audience. This paper describes the design and procedure of the competition, the results of the preliminary selection and live event of the competition, and the problems we identified from the event.
On the unification of the graph edit distance and graph matching problems

Error-tolerant graph matching gathers an important family of problems. These problems aim at finding correspondences between two graphs while integrating an error model. In the Graph Edit Distance (GED) problem, the insertion/deletion of edges/nodes from one graph to another is explicitly expressed by the error model. At the opposite, the problem commonly referred to as "graph matching" does not explicitly express such operations. For decades, these two problems have split the research community in two separated parts. It resulted in the design of different solvers for the two problems. In this paper, we propose a unification of both problems thanks to a single model. We give the proof that the two problems are equivalent under a reformulation of the error models. This unification makes possible the use on both problems of existing solving methods from the two communities.
Research on Application of Classification Model Based on Stack Generalization in Staging of Cervical Tissue Pathological Images

Cervical cancer is a malignant tumor that threatens women's health and life.Cervical pathology examination, as the gold standard for cervical cancer diagnosis, provides an important basis for the surgical plan and postoperative follow-up strategy for cervical cancer.Cervical biopsy diagnosis includes normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC).At present, cervical pathology examination still relies on the doctor's personal clinical experience and subjective judgment, which is time-consuming and may cause misdiagnosis or missed diagnosis.In addition, the current intelligent classification of cervical pathological images still has disadvantages such as imperfect classification system and low classification accuracy.Therefore, this experiment uses the ResNet50 model of the convolutional neural network as the feature extractor, and selects the K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM) classifiers in Machine Learning to perform cervical tissue pathological images Discrimination, the accuracy of the classification results were 85.83%, 80.33%, and 86.67%.In order to further improve the accuracy of the model and enhance the applicability and stability of the model, this experiment proposes the Stacked Generalization (SK) classification model.The first-layer base learner of the SK classification model selects CNN-KNN, CNN-RF, CNN-SVM, and the second-layer classifier selects Multilayer Perceptron (MLP).Among them, MLP makes the final result by learning the classification performance of the base learner for label discrimination, the accuracy of the classification model after ensemble learning is 90.00%.In addition, this experiment uses the Synthetic Minority Oversampling Technique (SMOTE) algorithm to amplify the training samples, and the amplified data set has a classification accuracy of 89.17% under the training of the SK classification model.The results show that the SK classification model in this experiment has a high classification ability for cervical histopathological images, and has good generalization ability and robustness.
Towards Understanding and Detecting Cyberbullying in Real-world Images

Cyberbullying has become widely recognized as a critical social problem plaguing today's Internet users.This problem involves perpetrators using Internet-based technologies to bully their victims by sharing cyberbullying-related content.To combat this problem, researchers have studied the factors associated with such content and proposed automatic detection techniques based on those factors.However, most of these studies have mainly focused on understanding the factors of textual content, such as comments and text messages, while largely overlooking the misuse of visual content in perpetrating cyberbullying.Recent technological advancements in the way users access the Internet have led to a new cyberbullying paradigm.Perpetrators can use visual media to bully their victims through sending and distributing images with cyberbullying content.As a first step to understand the threat of cyberbullying in images, we report in this paper a comprehensive study on the nature of images used in cyberbullying.We first collect a real-world cyberbullying images dataset with 19,300 valid images.We then analyze the images in our dataset and identify the factors related to cyberbullying images that can be used to build systems to detect cyberbullying in images.Our analysis of factors in cyberbullying images reveals that unlike traditional offensive image content (e.g., violence and nudity), the factors in cyberbullying images tend to be highly contextual.We further demonstrate the effectiveness of the factors by measuring several classifier models based on the identified factors.With respect to the cyberbullying factors identified in our work, the best classifier model based on multimodal classification achieves a mean detection accuracy of 93.36% on our cyberbullying images dataset.
QRAS: efficient resource allocation for task scheduling in cloud computing

Abstract Cloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.
Corporate Social Responsibility Based on Radial Basis Function Neural Network Evaluation Model of Low-Carbon Circular Economy Coupled Development

Under the background that the development of low-carbon circular economy is the objective requirement for the in-depth implementation of scientific development and the inevitable choice for promoting the sustainable development of economy and society, it is not only the requirement of corporate social responsibility but also the path to realize corporate social responsibility. Enterprises should become the representative and model of social responsibility practice in the development of low-carbon circular economy, in order to promote the fulfilment and development of corporate social responsibility in the whole society. Therefore, it is of great theoretical and practical significance to study the realization of corporate social responsibility in the context of low-carbon circular economy. This paper introduces the connotation of low-carbon circular economy and corporate social responsibility, analyses the reality and theoretical basis of realizing corporate social responsibility in low-carbon circular economy, analyses the interactive relationship between the development of low-carbon circular economy and the realization of corporate social responsibility, and puts forward the construction of enterprise low-carbon operation mechanism. This paper uses the research of corporate social responsibility based on radial basis function neural network to build a low-carbon circular economy. The evaluation model of environment economy coupling development is verified by an example, which provides useful guidance for the evaluation and development of corporate social responsibility.
Hierarchical and Self-Attended Sequence Autoencoder

It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posterior collapse</i> in variational inference. The input sequence is disregarded in the estimated latent variables. This paper proposes three components to tackle this difficulty and build the variational sequence autoencoder (VSAE) where sufficient latent information is learned for sophisticated sequence representation. First, the complementary encoders based on a long short-term memory (LSTM) and a pyramid bidirectional LSTM are merged to characterize global and structural dependencies of an input sequence, respectively. Second, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic self attention</i> mechanism is incorporated in a recurrent decoder. The latent information is attended to encourage the interaction between inference and generation in an encoder-decoder training procedure. Third, an autoregressive Gaussian prior of latent variable is used to preserve the information bound. Different variants of VSAE are proposed to mitigate the posterior collapse in sequence modeling. A series of experiments are conducted to demonstrate that the proposed individual and hybrid sequence autoencoders substantially improve the performance for variational sequential learning in language modeling and semantic understanding for document classification and summarization.
Review of Medical Image Synthesis using GAN Techniques

Generative Adversarial Networks (GANs) is one of the vital efficient methods for generating a massive, high-quality artificial picture. For diagnosing particular diseases in a medical image, a general problem is that it is expensive, usage of high radiation dosage, and time-consuming to collect data. Hence GAN is a deep learning method that has been developed for the image to image translation, i.e. from low-resolution to highresolution image, for example generating Magnetic resonance image (MRI) from computed tomography image (CT) and 7T from 3T MRI which can be used to obtain multimodal datasets from single modality. In this review paper, different GAN architectures were discussed for medical image analysis.
Modeling and understanding <scp>social–ecological</scp> knowledge diversity

Abstract The concept of social–ecological knowledge diversity (SEKD) provides a novel way of examining coupled human–environment interactions—it acknowledges differences in knowledge, values, and beliefs of stakeholder groups within social–ecological systems (SES). Thus, understanding and measuring SEKD is an essential component of sustainable management with implications for conflict resolution, collective action and policymaking. However, methods to efficiently define and model knowledge diversity are still underdeveloped. Using a semiquantitative cognitive mapping approach, we collected and analyzed stakeholder‐specific knowledge and perceptions of the Western Baltic cod fishery to model SEKD. Results demonstrate substantial variation in perceptions across different individuals and social groups. SEKD was evident in (a) distinctive meanings attached to social factors relative to ecological factors, (b) causal relationships underlying the understanding of SES dynamics, and (c) social impacts of ecological changes on ecosystems (and vice versa). By identifying and representing knowledge‐specific disparities in SES frameworks, our model explicitly improves the understanding of human–environment interactions with implications that could help reduce conflicts and legitimize management plans.
Bitcoin price prediction in the time of COVID-19

Based on the Bitcoin exchange data, COVID-19 data, and Twitter data from January 2020 to July 2020, this paper compares the performance of four different machine learning models on predicting the Bitcoin return rate and price trend. Data are formulated to four input feature sets, including: (1) Historical Bitcoin exchange data; (2) Historical Bitcoin exchange data + COVID-19 data (recovery, confirmed, death); (3) Historical Bitcoin exchange data + Twitter data; (4) Historical Bitcoin exchange data + COVID-19 data (recovery, confirmed, death) + Twitter data. The four machine learning models implemented are: (1) Random forest; (2) Decision tree; (3) AdaBoost; (4) Support vector machine. We found that: (1) Twitter data can improve the performance of models; (2) People consider information within 5 days when they make decisions on investments; (3) Support vector machine does not perform well in predicting Bitcoin return rate or price trend; (4) COVID-19 data does not help improve the prediction. However, we have very limited COVID-19 data, so future research with more COVID-19 data may help confirm if the last statement is correct or not.
Poisson Quantum Information

By taking a Poisson limit for a sequence of rare quantum objects, I derive simple formulas for the Uhlmann fidelity, the quantum Chernoff quantity, the relative entropy, and the Helstrom information. I also present analogous formulas in classical information theory for a Poisson model. An operator called the intensity operator emerges as the central quantity in the formalism to describe Poisson states. It behaves like a density operator but is unnormalized. The formulas in terms of the intensity operators not only resemble the general formulas in terms of the density operators, but also coincide with some existing definitions of divergences between unnormalized positive-semidefinite matrices. Furthermore, I show that the effects of certain channels on Poisson states can be described by simple maps for the intensity operators.