Reviewerly/exHarmony huggingface dataset:
THE INFLUENCE OF SECURITY CHALLENGES IN THE USE OF THE INTERNET AND MOBILE APPLICATIONS ON CREATING A SECURITY CULTURE

The growing number of threats and challenges from cyberspace, due to the increasing use of the Internet and mobile applications as never before, requires the implementation of security policies aimed at preserving the information and communication assets of the company. Security is achieved through one part of measures that are technical-technological in nature, and the other part is related to the actions of people in the organization. People are known to be the weakest link, and security culture aims to act on people by adopting certain behaviours, building habits and establishing communication in a way that they can respond to threats from cyberspace. Information security culture is established and maintained through programs that aim to prepare people for current and future challenges from cyberspace. One day, young people will start working, but until that moment, parents, in synergy with school institutions, should direct them so that they accept certain behaviours and norms. In this way, the security culture ensures that their security is preserved from the earliest days, so it is necessary to develop programs at different levels of education that will enable this.
Sustainable and resiliant coastal cities (SARCC)

Sustainable And Resilient Coastal Cities (SARCC) is an Interreg 2Seas co-funded project, which aims to mainstream nature-based solutions (NBS) into coastal management and policy making. Whether that is as stand-alone projects or integrated into existing grey infrastructure, known as Hybrid-NBS. The project aims to build capacity of urban leaders and decision makers involved with coastal flood defences, to deploy NBS and understand the benefits they offer in comparison to traditional infrastructure.Over years of R&D, Exo Environmental have previously developed GeoBlockTM technology, which uses inert waste aggregates such as dredged sediment or quarry by-products for concrete like cast products. These serve a multitude of environmental purposes, such as Eco reef units and Eco rock armour.As a project partner, Exo have developed unique surface textures which fit the aims set out by SARCC to create innovative Hybrid-NBS products. The developed products are unique surfaces which can be retrofitted to existing sea defences, encourage bio-colonisation and act as diversity enhancers when compared to conventional concrete or rock armour solutions. The use of natural base aggregate provides a ‘known’ substrate for the growth of biofilms and thus more complex ecosystems. Using 3D printed surface textures provides heterogeneity to the product design, increasing colonisation potential.Following calcareous bio-colonisation, the Eco products then act as bio-armouring units, with living organisms absorbing erosive wave energy and protecting the coastline from weathering processes. This is particularly important in areas of coastal squeeze, where there is limited space for similar habitats to develop and hard defences are required.Through unique research, Exo are contributing towards innovations in the Eco-Technology arena and supporting a circular economy of sustainability by reducing disposal costs and the associated carbon emissions. As well as creating products which serve as bio-diversity enhancers and perform vital services, such as erosion control.
Motion Trajectory Grouping for Human Head Gestures Related to Facial Expressions

The paper focuses on human head motion in connection with facial expressions for virtual-based interaction systems. Nowadays, the virtual representation of a human, with human-like social behaviour and mechanism of movements, can realize the user-machine interaction. The presented method includes the head motion because head gestures transmit additional information about the interaction’s situational context. This paper presents head motion analysis based on the rotation of rigid objects technique for virtual-based interaction systems. First, we captured the head gestures of a human subject, expressing three basic facial expressions. The proposed motion model was described using three non-deformable objects, which reflect the neck and head skeleton movement’s character. Based on the captured actions, the motion trajectories were analyzed, and their characteristic features were distinguished. The obtained dependencies were used to created new trajectories using piecewise cubic Hermite interpolating polynomial (PCHIP). Furthermore, the trajectories assigned to the rigid model have been grouped according to their similarities for a given emotional state. This way, using a single master trajectory and a set of coefficients, we were able to generate the whole set of trajectories for joint rotations of the head for the target emotional state. The resulting rotation trajectories were used to create movements on the three-dimensional human head.
A Perspective of Security Features in Amazon Web Services

In the recent times, increase in ICT tools has made significant progress in various sectors. As a result, data storage has become one of the most challenging tasks. Cloud computing has resolved this problem by providing on-demand computing power like virtual servers, storage space, function as a service, and many more. Cloud computing indisputably provides everlasting benefits and is cost-effective model. However, the major drawback in cloud computing is security. In this paper, we discuss about shared responsibility model, global infrastructure and compliance program, and network security features in cloud. These will act as a base for all the services provided by the Amazon Web Services (AWS). We have explored the security features in Amazon EC2, Amazon EBS, Amazon ELB, Amazon VPC, Amazon Route 53, AWS Direct Connect.
Higher Order Statistical Analysis in Multiresolution Domain - Application to Breast Cancer Histopathology

Objective is to analyze textures in breast histopathology images for cancer diagnosis.Background: It is observed that breast cancer has second highest mortality rate in women. Detection of cancer in early stages can give more treatment options and thus reduce the mortality rate. In cancer diagnosis using histopathology images, histologists examine biopsy samples based on cell morphology, tissue distribution, randomness in their growth or placements. These methods are time taking and sometime leads to incorrect diagnosis. These methods are highly subjective/arbitrary. The new techniques use computers, archived data and standard algorithms to provide fast and accurate results.Material & Methods: In this work we have proposed a multiresolution statistical model in wavelet domain. The primary idea is to study complex random field of histopathology images which contain long–range and nonlinear spatial interactions in wavelet domain. This model emphasizes the contribution of Gray level Run Length Matrix (GLRLM) and related higher order statistical features in wavelet subbands. The image samples are taken from ‘BreaKhis’ database. The standard database generated in collaboration with the P&D Laboratory—Pathological Anatomy and Cytopathology, Parana, Brazil. This study has been designed for breast cancer histopathology images of ductal carcinoma. GLRLM feature dataset further classified by SVM classifier with linear kernel. The classification accuracies of signal resolution and multiresolution have been compared.Results: The results show that the GLRLM based features provides exceptional distinguishing features for multiresolution analysis of histopathology images. Apart from recent deep learning method this study proposes use of higher order statistics to gain stronger image features. These features carry inherent discriminative properties. This higher order statistical model will be suitable for cancer detection.Conclusion: This work proposes automated diagnosis. Tumor spatial heterogeneity is the main concern in analyzing, diagnosing and grading cancer. This model focuses on Long range spatial dependencies in heterogeneous spatial process and offers solutions for accurate classification in two class problems. The work describes an innovative way of using GLRLM based textural features to extract underlying information in breast cancer images.
Mechanisms of Interaction and Integration of Universities into the Global Higher Education Space in the Modern World

The paper considers the topical issue related to mechanisms of interaction and integration of national and regional universities into the global higher education space. These processes require the development of new educational, research and commercial standards for the development of universities. This matter will enable to implement the promising ways of the development of national and regional higher education and implementation of the University 3.0 model in Russia. Global best practice defines coordinates and development level of national and regional universities. Structural and logical, systematic and methodological approaches on the standardization basis provide the framework for solution of problems in the sphere of university education. A complex of principles of university development (including global best practice through the developed “Economy of Knowledge and Innovation” course) is substantiated and tested. The optimal modular structure of this course, with practice-oriented tasks of innovative content as mandatory elements of each module, is defined and worked out. Universities 3.0 are very productive as the poles of economic life in the economy of knowledge and innovation, and as the centers of educational process in modern society. The ideas presented in the paper allow to make a statement that new mechanisms of interaction and integration of universities into the global higher education space will increase their role in Russian and regional economy, turning them to the poles of innovative development.
Application Analysis of Actual Scene Based on Analog Sending Device and Substation Client

Abstract At present, all provinces have put into use the analog messenger device. The system has been put into use in large quantities and on a large scale, which has brought a series of operation and maintenance problems for field staff. Based on the collocation of the analog sender and the substation client, the application of the analog sender in the actual scene is analyzed. Research shows that under the condition of ensuring no communication interruption, this collocation method can enable maintenance personnel to realize physical isolation of maintenance equipment and operation equipment, which greatly improves the safety of maintenance work. In addition, the use of analog signaling devices can significantly reduce the impact on the operating part of the commissioning process, reducing the pressure on maintenance personnel.
Attacking the Application of Three-Pass Protocol in Hill-Cipher

Hill-Cipher is one of the symmetric encryption algorithms that use one key for the encryption and decryption processes. This used key should be sent to the receiver in order to do the decryption process. However, to avoid sending the key to the receiver, three-pass protocol is applied. Three-pass protocol is a scheme to transmit data between the sender and receiver without having any advance distribution for the keys. In this study, a new attack on the encryption algorithm, three-pass protocol on Hill-Cipher, is introduced. This attack depends only on the ciphertext which makes us break this system by analyzing the encryption and decryption process.
An Accessible Evaluation Tool to Detect Easy-To-Read Barriers

People with cognitive and learning disabilities make up a significant percentage of the general population. Among the difficulties of this group, it is frequent that they present a poor comprehension of texts. The easy-to-read guidelines facilitate the comprehension of texts but adapting texts according to these rules is a time-consuming manual labour. There are automatic approaches to simplify texts and assess difficulty after the text is published, but there is not a tool to help writers and editors in the editorial process. In this paper a platform is presented to assist in this process by visually identifying problematic elements indicated in the guidelines. This approach allows publishers to do their job more efficiently.
VARIASI PENGGUNAAN REKONSTRUKSI ALGORITMA FBP, iDOSE4 DAN ITERATIVE MODEL RECONSTRUCTION (IMR) TERHADAP KUALITAS CITRA MSCT TORAKS LOW DOSE UNTUK DETEKSI NODUL PARU

Pulmonary nodules are often found accidentally when thorax imaging is doneduring screening or for other reasons. The size and rate of nodule growth are themost important predictors of imaging for malignancy. The low dose protocol appliedto the MSCT of the thorax will affect image quality, which is characterized byincreased noise and decreased spatial resolution, so that the detection of nodulesbecomes less accurate.For noise limitation, the reconstruction of the FBP iDose4and IMR algorirthm is used. The purpose of this literature study is to evaluatevariations in the FBP, iDose4 and IMR algorithm in improving image quality.The method used in this research is the study of literature by searchingjournals through Google Scholar, PubMed and Proquest. Then a comprehensivedescriptive data analysis is carried out containing studies os similirities, differencesand critique based on the view of the researcher and conveying suggestions that canbe given.The result of the literature study show that noise reduction is highest at IMRand lowest at FBP. The highest number of detected nodules on IMR especially forsolid nodules < 4 mm and the lowest on FBP. Pathological findings with the bestquality on IMR and quality are limited to FBP. Visibility of normal and abnormalfindings, iDose4 is better than IMR and FBP especially for reticular patterns. Thelevel of diagnostic confidence is the highest IMR improves image quality, second isiDose4 and third is FBP. Effective doses are reduced by 44 % to 59 % based on thisliterature study. So IMR is the most effective algorithm applied for scanning low doseMSCT thorax for detection of pulmonary nodules.
End to End Alignment Learning of Instructional Videos with Spatiotemporal Hybrid Encoding and Decoding Space Reduction

We solve the problem of how to densely align actions in videos at frame level, with only the order of occurring actions available, in order to save the time-consuming efforts to accurately annotate the temporal boundaries of each action. We propose three task-specific innovations under this scenario: (1) To encode fine-grained spatiotemporal local features and long-range temporal patterns simultaneously, we test three popular backbones and compare their accuracy and training times: (i) a recurrent LSTM; (ii) a fully convolutional model; and (iii) the recently proposed Transformer model. (2) To address the absence of ground truth frame-by-frame labels during training, we apply connectionist temporal classification (CTC) on top of the temporal encoder to recursively collect all theoretically valid alignments, and further weight these alignments with frame-wise visual similarities, in order to avoid a significant number of degenerated paths and improve both recognition accuracy and computation efficiency. (3) To quantitatively assess the quality of the learned alignment, we apply a comprehensive set of frame-level, segment-level, and video-level evaluation measurements. Extensive evaluations verify the effectiveness of our proposal, with performance comparable to that of fully supervised approaches across four benchmarks of different difficulty and data scale.
Fast and robust generation of a CNOT gate via transitionless quantum driving

In this paper, we present the generation of a CNOT gate between two atoms in a cavity based on transitionless quantum driving. Compared to some previous schemes, the proposed scheme for generating a CNOT gate significantly shortens the duration time. Besides, the scheme is insensitive to the fluctuation of evolution time and coupling coefficient. Also, the influences of atomic spontaneous emission and cavity decay on the fidelity are discussed by numerical simulation. The numerical result shows that the scheme is robust against decoherence.
Evaluation and Adaption of Maintenance Prediction Methods in Mixed Production Line Setups based on Anomaly Detection

Mixed production line setups are common in real world scenarios but are sparsely covered by current research in anomaly detection. A mixed production line setup poses several challenges and properties that make using anomaly detection more complex compared to monotonous production processes. We consider a high variation in the produced products, diluted temporal dependencies, imbalance in the frequency of product types and sensor measurements differing based on the produced product type. We gather contextual information using the OPC UA standard and extract information such as running program name. In this work we adapt and evaluate common anomaly detection methods to such a scenario. By building ensembles of anomaly detectors, we account for different setups. Each single work-piece gets evaluated for anomalous behaviour. We evaluate the anomaly detection by using data from a mixed production setup in the automotive domain. The context based approach is compared to a sliding window approach, which we use as a baseline. The results highlight that our product type-based approach shows a higher precision and recall for all applied detection techniques, by utilizing contextual information. Our experiments additionally show that in the selected industrial case study our approach achieves good results even when only limited data per product type is available. Index Terms-data mining, real-time system, maintenance prediction, adaptive network, reliability evaluation, sustainability.
Playbook: An Animation-Based Game for Improving Children's Creativity and Problem Solving Skills

In this paper, we describe the design, development and preliminary evaluation of a novel animation tool for smartphones and tablets, with the goal of improving creativity and problem-solving ability among novice users, mainly young children. The user first draws the objects with markers and crayons on paper and takes a photo with their mobile or tablet to import their drawings into the app as game sprites. They then design their animation scene and point to a part of the character and selecting its animation type from existing buttons. Hence the selected part is animated in a vertex shader while ensuring a smooth transition with the non-pointed part. The results of a preliminary evaluation showed that young children enjoyed interacting with our tool and would recommend it to their friends.
Identification of Sensitive Regions for Power Equipment Based on Fast R-CNN

Electricity is an indispensable resource in the daily life of people. However, the inspection of power equipment is still in the artificial stage. This way of work is inefficient, consumes a lot of manpower and material resources, and is not accurate enough. In order to realize the intellectualization of patrol inspection system, this paper uses a fast R-CNN technology based on Google LeNet V2, which brings a more efficient and advanced supervisory technology for power equipment. Experimental results of this paper show that the average accuracy rate of the scheme reaches 92.0%, and the average recall rate reaches 79.7%, with good results..
NBNN-Based Discriminative 3D Action and Gesture Recognition

The non-parametric models, e.g., Naive Bayes Nearest Neighbor (NBNN) Boiman et al. (Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 1–8, ), have achieved great success in object recognition problem. This success in object recognition motivates us to develop non-parametric model to recognize skeleton-based action and gesture sequences. In our proposed method, each action/gesture instance is represented by a set of temporal stage descriptors composed of features from spatial joints in a 3D pose. Considering the sparsity of involved joints in certain actions/gestures and the redundancy of stage descriptors, we choose Principal Component Analysis (PCA) as a pattern mining tool to pick out informative joints with high variance. To further boost the discriminative ability of the low-dimensional stage descriptor, we introduce the idea proposed in Yuan et al. () to help discriminative variation patterns learnt by PCA to emerge. Experiments on two benchmark datasets, MSR-Action 3D dataset and SBU Interaction dataset, show the efficiency of the proposed method. Evaluation on the SBU Interaction dataset shows that our method can achieve better performance than state-of-the-art results using sophisticated models such as deep learning.
Design of Flight Guidance and Control Systems Using Explainable AI

Advances in Artificial Intelligence (AI) have enabled the development of Flight Guidance and Control Systems (FG&CS) using Machine Learning (ML) from large archives of operational flight data. As the scope of the missions of drones/Small Unmanned Airborne Systems (sUAS) and Urban Air Mobility (UAM) vehicles increase (e.g. operations beyond-visual-line-of-sight (BVLOS) over people), the need for proof-of-compliance for safety regulations is required.Approaches to meet the regulatory airworthiness requirements for software assurance, such as those embodied in DO-178, require coverage of paths through the code. This "path coverage requirement" can be achieved by a sub-class of ML, known as Explainable ML (X-ML) that enables the derivation of formal models, such as Situation-Goal-Behavior Models (SGBM), from the ML FG&CS.This paper describes the results of a case-study of the development of an X-ML FG&CS that highlights the limits of an X-ML only approach and the need for a Model-based System Engineering (MBSE) analysis and design to complete the design to meet safety performance standards. The case study identified, specifically, the presence of five design error archetypes. These design error archetypes can be eliminated (i.e. addressed) by analysis of the SGBM using Model-Based System Engineering (MBSE) Tools as described in this paper. The implications and limitations of this approach for developing airworthy FG&CS are discussed.
The teacher-student relationship in the context of COVID-19 educational virtuality

In the context of the COVID-19 health crisis, the recent irruption of virtual education has highlighted the Chilean educational system's inequalities since technological devices mediate the affective interactions between teachers and students. From a feminist perspective, it becomes relevant to identify the importance of affection for teachers within their pedagogical practice, determine the use of technological means and resources from their democratizing possibility, and typologize the affective interactions between teachers and students’ virtual educational practices. This article's research axis corresponds to qualitative analysis with a phenomenological approach. The analysis technique is narrative-phenomenological, as audiovisual records of classes are collected, and units of meaning and categories are generated according to what is observed. During the research, we found four affective categories and eight sub-categories within the pedagogical practice linked to digital media use and, consequently, the affective implication that exists in the Chilean school educational model. The theoretical perspective is sustained from a feminist pedagogical approach, understood as an inflection point in front of the vertical and hierarchical logic of power—which characterizes the capitalist model. This perspective allows scrutinizing the pedagogical interaction as a phenomenon, revealing different experiences affected by the same problem, and making visible the different educational experiences existing within the educational banking model. This way, it enlights more horizontal and equitable educational forms.